{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scaling et al"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "wine = datasets.load_wine()\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    wine.data,\n",
    "    columns=wine.feature_names\n",
    ")\n",
    "\n",
    "df['target'] = pd.Series(\n",
    "    wine.target\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df_train, df_test = train_test_split(df, test_size=0.4, random_state=22)\n",
    "\n",
    "x_train = df_train[wine.feature_names]\n",
    "x_test  = df_test[wine.feature_names]\n",
    "\n",
    "y_train = df_train['target']\n",
    "y_test  = df_test['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "x_train_scaled = scaler.fit_transform(x_train)\n",
    "x_test_scaled = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(-1,1))\n",
    "x_train_scaled = scaler.fit_transform(x_train)\n",
    "x_test_scaled = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "scaler = RobustScaler()\n",
    "x_train_scaled = scaler.fit_transform(x_train)\n",
    "x_test_scaled = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mutual Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x1  x2  y\n",
       "0   0   1  0\n",
       "1   1   0  1\n",
       "2   1   1  0\n",
       "3   1   0  1\n",
       "4   0   0  1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mi = pd.DataFrame(\n",
    "    {\n",
    "        'x1': [0, 1, 1, 1, 0],\n",
    "        'x2': [1, 0, 1, 0, 0],\n",
    "        'y': [0, 1, 0, 1, 1],\n",
    "    }\n",
    ")\n",
    "\n",
    "df_mi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.78333333])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import mutual_info_classif\n",
    "\n",
    "mutual_info_classif(df_mi[['x1', 'x2']], df_mi['y'], n_neighbors=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.013844293808390418"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mutual_info_score\n",
    "\n",
    "mutual_info_score(df_mi['x1'], df_mi['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.013844293808390619\n"
     ]
    }
   ],
   "source": [
    "import math \n",
    "\n",
    "p_xy = lambda x, y, xcol: df_mi[(df_mi[xcol] == x) & (df_mi['y'] == y)].shape[0] / df_mi.shape[0]\n",
    "p_x = lambda x, xcol: df_mi[(df_mi[xcol] == x)].shape[0] / df_mi.shape[0]\n",
    "p_y = lambda y: df_mi[(df_mi['y'] == y)].shape[0] / df_mi.shape[0]\n",
    "\n",
    "def pmi(x, y, xcol='x1'):\n",
    "    _pxy = p_xy(x, y, xcol)\n",
    "    _px = p_x(x, xcol)\n",
    "    _py = p_y(y)\n",
    "    if _pxy == 0:\n",
    "        return 0\n",
    "    else:\n",
    "        return _pxy * math.log(_pxy / (_px * _py))\n",
    "\n",
    "mi = []\n",
    "for i in [0, 1]:\n",
    "    for j in [0, 1]:\n",
    "        mi.append(pmi(i, j, 'x1'))\n",
    "\n",
    "print(sum(mi))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "wine = datasets.load_wine()\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    wine.data,\n",
    "    columns=wine.feature_names\n",
    ")\n",
    "\n",
    "df['target'] = pd.Series(\n",
    "    wine.target\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df_train, df_test = train_test_split(df, test_size=0.4, random_state=22)\n",
    "\n",
    "x_train = df_train[wine.feature_names]\n",
    "x_test  = df_test[wine.feature_names]\n",
    "\n",
    "y_train = df_train['target']\n",
    "y_test  = df_test['target']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variance Threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VarianceThreshold(threshold=0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "\n",
    "vt = VarianceThreshold(threshold=0)\n",
    "vt.fit(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>nonflavanoid_phenols</td>\n",
       "      <td>0.013845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>hue</td>\n",
       "      <td>0.055709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ash</td>\n",
       "      <td>0.068327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>proanthocyanins</td>\n",
       "      <td>0.306081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>total_phenols</td>\n",
       "      <td>0.369353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>od280/od315_of_diluted_wines</td>\n",
       "      <td>0.478116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alcohol</td>\n",
       "      <td>0.679757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>flavanoids</td>\n",
       "      <td>0.925865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>malic_acid</td>\n",
       "      <td>0.987016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>color_intensity</td>\n",
       "      <td>5.612742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>alcalinity_of_ash</td>\n",
       "      <td>8.914152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>magnesium</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>proline</td>\n",
       "      <td>1402.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Feature     Variance\n",
       "7           nonflavanoid_phenols     0.013845\n",
       "10                           hue     0.055709\n",
       "2                            ash     0.068327\n",
       "8                proanthocyanins     0.306081\n",
       "5                  total_phenols     0.369353\n",
       "11  od280/od315_of_diluted_wines     0.478116\n",
       "0                        alcohol     0.679757\n",
       "6                     flavanoids     0.925865\n",
       "1                     malic_acid     0.987016\n",
       "9                color_intensity     5.612742\n",
       "3              alcalinity_of_ash     8.914152\n",
       "4                      magnesium    92.000000\n",
       "12                       proline  1402.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    {\n",
    "        'Feature': wine.feature_names,\n",
    "        'Variance': vt.variances_,\n",
    "    }\n",
    ").sort_values(\n",
    "    'Variance', ascending=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VarianceThreshold(threshold=5)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vt = VarianceThreshold(threshold=5)\n",
    "vt.fit(x_train)\n",
    "# x_train_new, x_test_new = vt.transform(x_train), vt.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>20.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>5.40</td>\n",
       "      <td>530.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>6.20</td>\n",
       "      <td>1280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>19.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.40</td>\n",
       "      <td>510.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>19.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>17.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>3.17</td>\n",
       "      <td>750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>21.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>17.5</td>\n",
       "      <td>97.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>17.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>5.04</td>\n",
       "      <td>885.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>24.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>5.70</td>\n",
       "      <td>560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>22.5</td>\n",
       "      <td>108.0</td>\n",
       "      <td>2.06</td>\n",
       "      <td>345.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>106 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcalinity_of_ash  magnesium  color_intensity  proline\n",
       "131               20.0      104.0             5.40    530.0\n",
       "16                20.0      120.0             6.20   1280.0\n",
       "106               19.0       80.0             3.40    510.0\n",
       "64                19.0      104.0             2.95    355.0\n",
       "68                17.0      110.0             3.17    750.0\n",
       "..                 ...        ...              ...      ...\n",
       "102               21.0       98.0             2.80    438.0\n",
       "100               17.5       97.0             3.30    710.0\n",
       "44                17.0      107.0             5.04    885.0\n",
       "132               24.0       98.0             5.70    560.0\n",
       "117               22.5      108.0             2.06    345.0\n",
       "\n",
       "[106 rows x 4 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# x_train_new = x_train.iloc[:, vt.get_support()]\n",
    "x_train.iloc[:, vt.get_support()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VAR(units): 8.25, VAR(tens): 825.0, VAR(hundreds): 82500.0\n"
     ]
    }
   ],
   "source": [
    "units = np.arange(10)\n",
    "tens = 10 * units\n",
    "hundreds = 100 * units\n",
    "\n",
    "print(\n",
    "    'VAR(units): {}, VAR(tens): {}, VAR(hundreds): {}'.format(\n",
    "        units.var(), tens.var(), hundreds.var()\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VarianceThreshold(threshold=0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "x_train_scaled = scaler.fit_transform(x_train)\n",
    "\n",
    "vt = VarianceThreshold(threshold=0)\n",
    "vt.fit(x_train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>magnesium</td>\n",
       "      <td>0.027419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ash</td>\n",
       "      <td>0.029189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>alcalinity_of_ash</td>\n",
       "      <td>0.032743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>hue</td>\n",
       "      <td>0.036823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>proanthocyanins</td>\n",
       "      <td>0.037160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>flavanoids</td>\n",
       "      <td>0.041209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>total_phenols</td>\n",
       "      <td>0.043918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>color_intensity</td>\n",
       "      <td>0.044269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alcohol</td>\n",
       "      <td>0.047075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>malic_acid</td>\n",
       "      <td>0.049843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>nonflavanoid_phenols</td>\n",
       "      <td>0.051202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>proline</td>\n",
       "      <td>0.053753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>od280/od315_of_diluted_wines</td>\n",
       "      <td>0.068083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Feature  Variance\n",
       "4                      magnesium  0.027419\n",
       "2                            ash  0.029189\n",
       "3              alcalinity_of_ash  0.032743\n",
       "10                           hue  0.036823\n",
       "8                proanthocyanins  0.037160\n",
       "6                     flavanoids  0.041209\n",
       "5                  total_phenols  0.043918\n",
       "9                color_intensity  0.044269\n",
       "0                        alcohol  0.047075\n",
       "1                     malic_acid  0.049843\n",
       "7           nonflavanoid_phenols  0.051202\n",
       "12                       proline  0.053753\n",
       "11  od280/od315_of_diluted_wines  0.068083"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    {\n",
    "        'Feature': wine.feature_names,\n",
    "        'Variance': vt.variances_,\n",
    "    }\n",
    ").sort_values(\n",
    "    'Variance', ascending=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>var</th>\n",
       "      <th>cv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alcohol</th>\n",
       "      <td>0.686231</td>\n",
       "      <td>0.063844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ash</th>\n",
       "      <td>0.068978</td>\n",
       "      <td>0.111398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>magnesium</th>\n",
       "      <td>234.287960</td>\n",
       "      <td>0.153195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <td>8.999049</td>\n",
       "      <td>0.154009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hue</th>\n",
       "      <td>0.056240</td>\n",
       "      <td>0.242091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <td>0.482670</td>\n",
       "      <td>0.264152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_phenols</th>\n",
       "      <td>0.372870</td>\n",
       "      <td>0.267334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <td>0.013977</td>\n",
       "      <td>0.327457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>proanthocyanins</th>\n",
       "      <td>0.308996</td>\n",
       "      <td>0.351651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>proline</th>\n",
       "      <td>106662.478347</td>\n",
       "      <td>0.440263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malic_acid</th>\n",
       "      <td>0.996416</td>\n",
       "      <td>0.465610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flavanoids</th>\n",
       "      <td>0.934683</td>\n",
       "      <td>0.472344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color_intensity</th>\n",
       "      <td>5.666196</td>\n",
       "      <td>0.477743</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        var        cv\n",
       "alcohol                            0.686231  0.063844\n",
       "ash                                0.068978  0.111398\n",
       "magnesium                        234.287960  0.153195\n",
       "alcalinity_of_ash                  8.999049  0.154009\n",
       "hue                                0.056240  0.242091\n",
       "od280/od315_of_diluted_wines       0.482670  0.264152\n",
       "total_phenols                      0.372870  0.267334\n",
       "nonflavanoid_phenols               0.013977  0.327457\n",
       "proanthocyanins                    0.308996  0.351651\n",
       "proline                       106662.478347  0.440263\n",
       "malic_acid                         0.996416  0.465610\n",
       "flavanoids                         0.934683  0.472344\n",
       "color_intensity                    5.666196  0.477743"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coefficient of variation\n",
    "\n",
    "df_variances = x_train.apply([np.mean, np.var, np.std]).T\n",
    "df_variances['cv'] = df_variances['std'] / df_variances['mean']\n",
    "\n",
    "df_variances.sort_values('cv', ascending=True)[\n",
    "    ['var', 'cv']\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import f_classif, f_regression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "x_train_scaled = scaler.fit_transform(x_train)\n",
    "\n",
    "f, p = f_classif(x_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import mutual_info_classif\n",
    "\n",
    "mi = mutual_info_classif(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "        }</style><table id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Feature</th>        <th class=\"col_heading level0 col1\" >F</th>        <th class=\"col_heading level0 col2\" >MI</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922level0_row0\" class=\"row_heading level0 row0\" >6</th>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row0_col0\" class=\"data row0 col0\" >flavanoids</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row0_col1\" class=\"data row0 col1\" >119.566</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row0_col2\" class=\"data row0 col2\" >0.682623</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922level0_row1\" class=\"row_heading level0 row1\" >9</th>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row1_col0\" class=\"data row1 col0\" >color_intensity</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row1_col1\" class=\"data row1 col1\" >74.0814</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row1_col2\" class=\"data row1 col2\" >0.625665</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922level0_row2\" class=\"row_heading level0 row2\" >12</th>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row2_col0\" class=\"data row2 col0\" >proline</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row2_col1\" class=\"data row2 col1\" >133.289</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row2_col2\" class=\"data row2 col2\" >0.532535</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922level0_row3\" class=\"row_heading level0 row3\" >11</th>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row3_col0\" class=\"data row3 col0\" >od280/od315_of_diluted_wines</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row3_col1\" class=\"data row3 col1\" >90.6593</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row3_col2\" class=\"data row3 col2\" >0.498819</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922level0_row4\" class=\"row_heading level0 row4\" >0</th>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row4_col0\" class=\"data row4 col0\" >alcohol</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row4_col1\" class=\"data row4 col1\" >82.6288</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row4_col2\" class=\"data row4 col2\" >0.454655</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922level0_row5\" class=\"row_heading level0 row5\" >10</th>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row5_col0\" class=\"data row5 col0\" >hue</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row5_col1\" class=\"data row5 col1\" >66.7377</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row5_col2\" class=\"data row5 col2\" >0.426239</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922level0_row6\" class=\"row_heading level0 row6\" >5</th>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row6_col0\" class=\"data row6 col0\" >total_phenols</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row6_col1\" class=\"data row6 col1\" >54.6519</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row6_col2\" class=\"data row6 col2\" >0.36232</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922level0_row7\" class=\"row_heading level0 row7\" >1</th>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row7_col0\" class=\"data row7 col0\" >malic_acid</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row7_col1\" class=\"data row7 col1\" >23.0863</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row7_col2\" class=\"data row7 col2\" >0.352976</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922level0_row8\" class=\"row_heading level0 row8\" >3</th>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row8_col0\" class=\"data row8 col0\" >alcalinity_of_ash</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row8_col1\" class=\"data row8 col1\" >25.8039</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row8_col2\" class=\"data row8 col2\" >0.288957</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922level0_row9\" class=\"row_heading level0 row9\" >4</th>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row9_col0\" class=\"data row9 col0\" >magnesium</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row9_col1\" class=\"data row9 col1\" >7.65196</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row9_col2\" class=\"data row9 col2\" >0.26746</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922level0_row10\" class=\"row_heading level0 row10\" >8</th>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row10_col0\" class=\"data row10 col0\" >proanthocyanins</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row10_col1\" class=\"data row10 col1\" >15.1593</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row10_col2\" class=\"data row10 col2\" >0.243541</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922level0_row11\" class=\"row_heading level0 row11\" >7</th>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row11_col0\" class=\"data row11 col0\" >nonflavanoid_phenols</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row11_col1\" class=\"data row11 col1\" >9.79048</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row11_col2\" class=\"data row11 col2\" >0.134345</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922level0_row12\" class=\"row_heading level0 row12\" >2</th>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row12_col0\" class=\"data row12 col0\" >ash</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row12_col1\" class=\"data row12 col1\" >8.69691</td>\n",
       "                        <td id=\"T_a6eb07ac_ba27_11ea_bb2d_784f43517922row12_col2\" class=\"data row12 col2\" >0.119169</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7ff6dbdd88d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    {\n",
    "        'Feature': wine.feature_names,\n",
    "        'F': f,\n",
    "        'MI': mi,\n",
    "    }\n",
    ").sort_values(\n",
    "    'MI', ascending=False\n",
    ").style.bar(\n",
    "    subset=['F', 'MI'], color='grey'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/ipykernel_launcher.py:24: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(14, 8));\n",
    "\n",
    "df.plot(\n",
    "    kind='scatter',\n",
    "    x='flavanoids',\n",
    "    y='target',\n",
    "    ax=axs[0]\n",
    ")\n",
    "\n",
    "df.plot(\n",
    "    kind='scatter',\n",
    "    x='color_intensity',\n",
    "    y='target',\n",
    "    ax=axs[1]\n",
    ")\n",
    "\n",
    "df.plot(\n",
    "    kind='scatter',\n",
    "    x='proline',\n",
    "    y='target',\n",
    "    ax=axs[2]\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/ipykernel_launcher.py:35: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(14, 8));\n",
    "\n",
    "for c in df['target'].value_counts().index.tolist():\n",
    "    \n",
    "    df[\n",
    "        df['target'] == c\n",
    "    ].plot(\n",
    "        kind='scatter',\n",
    "        x='flavanoids',\n",
    "        y='proline',\n",
    "        color=['r', 'g', 'b'][c],\n",
    "        marker=f'${c}$',\n",
    "        s=64,\n",
    "        alpha='0.5',\n",
    "        label=f'Class {c}',\n",
    "        ax=axs[0],\n",
    "    )\n",
    "\n",
    "for c in df['target'].value_counts().index.tolist():\n",
    "    \n",
    "    df[\n",
    "        df['target'] == c\n",
    "    ].plot(\n",
    "        kind='scatter',\n",
    "        x='flavanoids',\n",
    "        y='color_intensity',\n",
    "        color=['r', 'g', 'b'][c],\n",
    "        marker=f'${c}$',\n",
    "        s=64,\n",
    "        alpha='0.5',\n",
    "        label=f'Class {c}',\n",
    "        ax=axs[1],\n",
    "    )\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>12.88</td>\n",
       "      <td>2.99</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.22</td>\n",
       "      <td>5.40</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1.42</td>\n",
       "      <td>530.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14.30</td>\n",
       "      <td>1.92</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.14</td>\n",
       "      <td>6.20</td>\n",
       "      <td>1.07</td>\n",
       "      <td>2.65</td>\n",
       "      <td>1280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>12.25</td>\n",
       "      <td>1.73</td>\n",
       "      <td>1.65</td>\n",
       "      <td>2.03</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.17</td>\n",
       "      <td>510.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>12.17</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.95</td>\n",
       "      <td>1.45</td>\n",
       "      <td>2.23</td>\n",
       "      <td>355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>13.34</td>\n",
       "      <td>0.94</td>\n",
       "      <td>2.53</td>\n",
       "      <td>1.30</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.93</td>\n",
       "      <td>750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>12.34</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.56</td>\n",
       "      <td>2.11</td>\n",
       "      <td>2.80</td>\n",
       "      <td>0.80</td>\n",
       "      <td>3.38</td>\n",
       "      <td>438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>12.08</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2.23</td>\n",
       "      <td>2.17</td>\n",
       "      <td>3.30</td>\n",
       "      <td>1.27</td>\n",
       "      <td>2.96</td>\n",
       "      <td>710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>13.05</td>\n",
       "      <td>1.77</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.04</td>\n",
       "      <td>0.88</td>\n",
       "      <td>3.35</td>\n",
       "      <td>885.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>12.81</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.09</td>\n",
       "      <td>5.70</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.36</td>\n",
       "      <td>560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>12.42</td>\n",
       "      <td>1.61</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.06</td>\n",
       "      <td>1.06</td>\n",
       "      <td>2.96</td>\n",
       "      <td>345.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>106 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol  malic_acid  total_phenols  flavanoids  color_intensity   hue  \\\n",
       "131    12.88        2.99           1.30        1.22             5.40  0.74   \n",
       "16     14.30        1.92           2.80        3.14             6.20  1.07   \n",
       "106    12.25        1.73           1.65        2.03             3.40  1.00   \n",
       "64     12.17        1.45           1.89        1.75             2.95  1.45   \n",
       "68     13.34        0.94           2.53        1.30             3.17  1.02   \n",
       "..       ...         ...            ...         ...              ...   ...   \n",
       "102    12.34        2.45           2.56        2.11             2.80  0.80   \n",
       "100    12.08        2.08           2.23        2.17             3.30  1.27   \n",
       "44     13.05        1.77           3.00        3.00             5.04  0.88   \n",
       "132    12.81        2.31           1.15        1.09             5.70  0.66   \n",
       "117    12.42        1.61           2.00        2.09             2.06  1.06   \n",
       "\n",
       "     od280/od315_of_diluted_wines  proline  \n",
       "131                          1.42    530.0  \n",
       "16                           2.65   1280.0  \n",
       "106                          3.17    510.0  \n",
       "64                           2.23    355.0  \n",
       "68                           1.93    750.0  \n",
       "..                            ...      ...  \n",
       "102                          3.38    438.0  \n",
       "100                          2.96    710.0  \n",
       "44                           3.35    885.0  \n",
       "132                          1.36    560.0  \n",
       "117                          2.96    345.0  \n",
       "\n",
       "[106 rows x 8 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import mutual_info_classif\n",
    "\n",
    "selector = SelectKBest(score_func=mutual_info_classif, k=8)\n",
    "selector.fit(x_train, y_train)\n",
    "\n",
    "x_train.iloc[:, selector.get_support()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression (MI top 5) Accuracy: 0.90\n",
      "Logistic Regression (F top 5) Accuracy: 0.97\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.feature_selection import mutual_info_classif\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "topn = 5\n",
    "\n",
    "# mutual_info_classif\n",
    "\n",
    "selector = SelectKBest(score_func=mutual_info_classif, k=2)\n",
    "selector.fit(x_train, y_train)\n",
    "\n",
    "x_train_topn = x_train.iloc[:, selector.get_support()]\n",
    "x_test_topn = x_test.iloc[:, selector.get_support()]\n",
    "\n",
    "scaler = StandardScaler()\n",
    "x_train_topn_scaled = scaler.fit_transform(x_train_topn)\n",
    "x_test_topn_scaled = scaler.transform(x_test_topn)\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(x_train_topn_scaled, y_train)\n",
    "y_pred = model.predict(x_test_topn_scaled)\n",
    "\n",
    "lr_mi_accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\n",
    "    f'Logistic Regression (MI top {topn}) Accuracy: {lr_mi_accuracy:.2f}'\n",
    ")\n",
    "\n",
    "# f_classif\n",
    "\n",
    "selector = SelectKBest(score_func=f_classif, k=topn)\n",
    "selector.fit(x_train, y_train)\n",
    "\n",
    "x_train_topn = x_train.iloc[:, selector.get_support()]\n",
    "x_test_topn = x_test.iloc[:, selector.get_support()]\n",
    "\n",
    "scaler = StandardScaler()\n",
    "x_train_topn_scaled = scaler.fit_transform(x_train_topn)\n",
    "x_test_topn_scaled = scaler.transform(x_test_topn)\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(x_train_topn_scaled, y_train)\n",
    "y_pred = model.predict(x_test_topn_scaled)\n",
    "\n",
    "lr_f_accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\n",
    "    f'Logistic Regression (F top {topn}) Accuracy: {lr_f_accuracy:.2f}'\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Classifier (MI top 4) Accuracy: 0.93\n",
      "Decision Tree Classifier (F top 4) Accuracy: 0.89\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.feature_selection import mutual_info_classif\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "topn = 4\n",
    "\n",
    "# mutual_info_classif\n",
    "\n",
    "selector = SelectKBest(score_func=mutual_info_classif, k=2)\n",
    "selector.fit(x_train, y_train)\n",
    "\n",
    "x_train_topn = x_train.iloc[:, selector.get_support()]\n",
    "x_test_topn = x_test.iloc[:, selector.get_support()]\n",
    "\n",
    "scaler = StandardScaler()\n",
    "x_train_topn_scaled = scaler.fit_transform(x_train_topn)\n",
    "x_test_topn_scaled = scaler.transform(x_test_topn)\n",
    "\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(x_train_topn_scaled, y_train)\n",
    "y_pred = model.predict(x_test_topn_scaled)\n",
    "\n",
    "dt_mi_accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\n",
    "    f'Decision Tree Classifier (MI top {topn}) Accuracy: {dt_mi_accuracy:.2f}'\n",
    ")\n",
    "\n",
    "# f_classif\n",
    "\n",
    "selector = SelectKBest(score_func=f_classif, k=topn)\n",
    "selector.fit(x_train, y_train)\n",
    "\n",
    "x_train_topn = x_train.iloc[:, selector.get_support()]\n",
    "x_test_topn = x_test.iloc[:, selector.get_support()]\n",
    "\n",
    "scaler = StandardScaler()\n",
    "x_train_topn_scaled = scaler.fit_transform(x_train_topn)\n",
    "x_test_topn_scaled = scaler.transform(x_test_topn)\n",
    "\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(x_train_topn_scaled, y_train)\n",
    "y_pred = model.predict(x_test_topn_scaled)\n",
    "\n",
    "dt_f_accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\n",
    "    f'Decision Tree Classifier (F top {topn}) Accuracy: {dt_f_accuracy:.2f}'\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_a8cb37fe_ba27_11ea_9220_784f43517922row0_col0 {\n",
       "            font-weight:  bold;\n",
       "        }    #T_a8cb37fe_ba27_11ea_9220_784f43517922row1_col1 {\n",
       "            font-weight:  bold;\n",
       "        }</style><table id=\"T_a8cb37fe_ba27_11ea_9220_784f43517922\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Logistic Regression</th>        <th class=\"col_heading level0 col1\" >Decision Tree Classifier</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_a8cb37fe_ba27_11ea_9220_784f43517922level0_row0\" class=\"row_heading level0 row0\" >SelectKBest(f_classif)</th>\n",
       "                        <td id=\"T_a8cb37fe_ba27_11ea_9220_784f43517922row0_col0\" class=\"data row0 col0\" >0.97</td>\n",
       "                        <td id=\"T_a8cb37fe_ba27_11ea_9220_784f43517922row0_col1\" class=\"data row0 col1\" >0.89</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a8cb37fe_ba27_11ea_9220_784f43517922level0_row1\" class=\"row_heading level0 row1\" >SelectKBest(mutual_info_classif)</th>\n",
       "                        <td id=\"T_a8cb37fe_ba27_11ea_9220_784f43517922row1_col0\" class=\"data row1 col0\" >0.9</td>\n",
       "                        <td id=\"T_a8cb37fe_ba27_11ea_9220_784f43517922row1_col1\" class=\"data row1 col1\" >0.93</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7ff6dbf9c400>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def highlight_max(s):\n",
    "    is_max = s == s.max()\n",
    "    return ['font-weight: bold' if v else '' for v in is_max]\n",
    "\n",
    "pd.DataFrame(\n",
    "    {\n",
    "        'Logistic Regression': [lr_f_accuracy, lr_mi_accuracy],\n",
    "        'Decision Tree Classifier': [dt_f_accuracy, dt_mi_accuracy], \n",
    "    },\n",
    "    index=['SelectKBest(f_classif)', 'SelectKBest(mutual_info_classif)'],\n",
    ").round(2).style.apply(highlight_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Accuracy 0.9444444444444444\n",
      "Decision Tree Classifier Accuracy 0.8611111111111112\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:939: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html.\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import mutual_info_classif\n",
    "\n",
    "selector = SelectKBest(score_func=mutual_info_classif, k=3)\n",
    "selector.fit(x_train, y_train)\n",
    "\n",
    "x_train_topn = x_train.iloc[:, selector.get_support()]\n",
    "x_test_topn = x_test.iloc[:, selector.get_support()]\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(x_train_topn, y_train)\n",
    "y_pred = model.predict(x_test_topn)\n",
    "\n",
    "print(\n",
    "    'Logistic Regression Accuracy',\n",
    "    accuracy_score(y_test, y_pred)\n",
    ")\n",
    "\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(x_train_topn, y_train)\n",
    "y_pred = model.predict(x_test_topn)\n",
    "\n",
    "print(\n",
    "    'Decision Tree Classifier Accuracy',\n",
    "    accuracy_score(y_test, y_pred)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.feature_selection import SelectKBest\n",
    "# feature_selection.SelectPercentile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Embedded "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SelectFromModel(estimator=DecisionTreeClassifier(ccp_alpha=0.0,\n",
       "                                                 class_weight=None,\n",
       "                                                 criterion='gini',\n",
       "                                                 max_depth=None,\n",
       "                                                 max_features=None,\n",
       "                                                 max_leaf_nodes=None,\n",
       "                                                 min_impurity_decrease=0.0,\n",
       "                                                 min_impurity_split=None,\n",
       "                                                 min_samples_leaf=1,\n",
       "                                                 min_samples_split=2,\n",
       "                                                 min_weight_fraction_leaf=0.0,\n",
       "                                                 presort='deprecated',\n",
       "                                                 random_state=None,\n",
       "                                                 splitter='best'),\n",
       "                max_features=8, norm_order=1, prefit=False, threshold=-inf)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "estimator = DecisionTreeClassifier()\n",
    "selector = SelectFromModel(estimator=estimator, threshold=-np.inf, max_features=8)\n",
    "selector.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>ash</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>12.88</td>\n",
       "      <td>2.40</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.83</td>\n",
       "      <td>5.40</td>\n",
       "      <td>0.74</td>\n",
       "      <td>530.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14.30</td>\n",
       "      <td>2.72</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.14</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.20</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>12.25</td>\n",
       "      <td>2.12</td>\n",
       "      <td>1.65</td>\n",
       "      <td>2.03</td>\n",
       "      <td>1.63</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1.00</td>\n",
       "      <td>510.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>12.17</td>\n",
       "      <td>2.53</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.03</td>\n",
       "      <td>2.95</td>\n",
       "      <td>1.45</td>\n",
       "      <td>355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>13.34</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.53</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.42</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1.02</td>\n",
       "      <td>750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>12.34</td>\n",
       "      <td>2.46</td>\n",
       "      <td>2.56</td>\n",
       "      <td>2.11</td>\n",
       "      <td>1.31</td>\n",
       "      <td>2.80</td>\n",
       "      <td>0.80</td>\n",
       "      <td>438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>12.08</td>\n",
       "      <td>1.70</td>\n",
       "      <td>2.23</td>\n",
       "      <td>2.17</td>\n",
       "      <td>1.40</td>\n",
       "      <td>3.30</td>\n",
       "      <td>1.27</td>\n",
       "      <td>710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>13.05</td>\n",
       "      <td>2.10</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.03</td>\n",
       "      <td>5.04</td>\n",
       "      <td>0.88</td>\n",
       "      <td>885.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>12.81</td>\n",
       "      <td>2.40</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.83</td>\n",
       "      <td>5.70</td>\n",
       "      <td>0.66</td>\n",
       "      <td>560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>12.42</td>\n",
       "      <td>2.19</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.09</td>\n",
       "      <td>1.61</td>\n",
       "      <td>2.06</td>\n",
       "      <td>1.06</td>\n",
       "      <td>345.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>106 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol   ash  total_phenols  flavanoids  proanthocyanins  \\\n",
       "131    12.88  2.40           1.30        1.22             0.83   \n",
       "16     14.30  2.72           2.80        3.14             1.97   \n",
       "106    12.25  2.12           1.65        2.03             1.63   \n",
       "64     12.17  2.53           1.89        1.75             1.03   \n",
       "68     13.34  2.36           2.53        1.30             0.42   \n",
       "..       ...   ...            ...         ...              ...   \n",
       "102    12.34  2.46           2.56        2.11             1.31   \n",
       "100    12.08  1.70           2.23        2.17             1.40   \n",
       "44     13.05  2.10           3.00        3.00             2.03   \n",
       "132    12.81  2.40           1.15        1.09             0.83   \n",
       "117    12.42  2.19           2.00        2.09             1.61   \n",
       "\n",
       "     color_intensity   hue  proline  \n",
       "131             5.40  0.74    530.0  \n",
       "16              6.20  1.07   1280.0  \n",
       "106             3.40  1.00    510.0  \n",
       "64              2.95  1.45    355.0  \n",
       "68              3.17  1.02    750.0  \n",
       "..               ...   ...      ...  \n",
       "102             2.80  0.80    438.0  \n",
       "100             3.30  1.27    710.0  \n",
       "44              5.04  0.88    885.0  \n",
       "132             5.70  0.66    560.0  \n",
       "117             2.06  1.06    345.0  \n",
       "\n",
       "[106 rows x 8 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.iloc[:, selector.get_support()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA & LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 500\n",
    "\n",
    "df_rand = pd.DataFrame(\n",
    "    {\n",
    "        'x1': np.random.normal(loc=0, scale=.1, size=n),\n",
    "        'x2': np.random.normal(loc=0, scale=.1, size=n),\n",
    "    }\n",
    ")\n",
    "\n",
    "df_rand['y'] = df_rand['x1'] - df_rand['x2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/ipykernel_launcher.py:12: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  if sys.path[0] == '':\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(14, 8), sharex=True, sharey=True);\n",
    "\n",
    "for i in range(2):\n",
    "    \n",
    "    df_rand.plot(\n",
    "        kind='scatter',\n",
    "        x=f'x{i+1}',\n",
    "        y='y',\n",
    "        ax=axs[i]\n",
    "    )\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.decomposition import KernelPCA\n",
    "\n",
    "n_components = 1\n",
    "\n",
    "pca = PCA(n_components=n_components)\n",
    "\n",
    "if n_components == 2:\n",
    "    df_rand['Component 1'] = pca.fit_transform(df_rand[['x1', 'x2']])[:,0]\n",
    "    df_rand['Component 2'] = pca.fit_transform(df_rand[['x1', 'x2']])[:,1]\n",
    "else:\n",
    "    df_rand['Principal Component'] = pca.fit_transform(df_rand[['x1', 'x2']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[-0.05722933, -0.99836106]]), array([0.01058689]))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_, pca.explained_variance_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/ipykernel_launcher.py:24: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(14, 8));\n",
    "    \n",
    "df_rand.plot(\n",
    "    kind='scatter',\n",
    "    x='x1',\n",
    "    y='y',\n",
    "    ax=axs[0]\n",
    ")\n",
    "\n",
    "df_rand.plot(\n",
    "    kind='scatter',\n",
    "    x='x2',\n",
    "    y='y',\n",
    "    ax=axs[1]\n",
    ")\n",
    "\n",
    "df_rand.plot(\n",
    "    kind='scatter',\n",
    "    x='Principal Component',\n",
    "    y='y',\n",
    "    ax=axs[2]\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "wine = datasets.load_wine()\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    iris.data,\n",
    "    columns=iris.feature_names\n",
    ")\n",
    "\n",
    "df['target'] = pd.Series(\n",
    "    iris.target\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = df[iris.feature_names], df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>ABS(Coeff)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>petal length (cm)</td>\n",
       "      <td>1.930629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>petal width (cm)</td>\n",
       "      <td>1.811689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sepal width (cm)</td>\n",
       "      <td>1.160063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sepal length (cm)</td>\n",
       "      <td>1.074041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Features  ABS(Coeff)\n",
       "2  petal length (cm)    1.930629\n",
       "3   petal width (cm)    1.811689\n",
       "1   sepal width (cm)    1.160063\n",
       "0  sepal length (cm)    1.074041"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "scaler = StandardScaler()\n",
    "model = LogisticRegression()\n",
    "model.fit(scaler.fit_transform(x), y)\n",
    "\n",
    "pd.DataFrame(\n",
    "    {\n",
    "        'Features': x.columns,\n",
    "        'ABS(Coeff)': np.abs(model.coef_[0]),\n",
    "    }\n",
    ").sort_values('ABS(Coeff)', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.decomposition import KernelPCA\n",
    "\n",
    "n_components = 2\n",
    "\n",
    "scaler = StandardScaler()\n",
    "pca = PCA(n_components=n_components)\n",
    "# pca = KernelPCA(n_components=n_components, kernel='cosine')\n",
    "\n",
    "x_scaled = scaler.fit_transform(x)\n",
    "x_pca = pca.fit_transform(x_scaled)\n",
    "\n",
    "df_pca = pd.DataFrame(\n",
    "    x_pca,\n",
    "    columns=[f'Component {n}' for n in range(1, n_components+1)]\n",
    ")\n",
    "df_pca['target'] = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/ipykernel_launcher.py:33: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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REUmuwSrhyuCcdAeQK06fPs3999/PrFmzWLBgAXfeeSfvvfcex48fZ968eUkZs7Ozk/vuu4/rrruOm266iePHjydlHJFs1feZjGXLUvdMxpkzscSzuRn+7u86uOYaFRoSERERyc87n9bivPEGY/buxTduHNFbb8VOnjyK7ix33303d911Fxs2bADg4MGDNDY2ctlll3kV9UV+/vOfM378eN58801+9atf8bd/+7ds3LgxaeOJZJt0PZPxjW+M4fRpQ3m5y5o1AQCuu66L5ct1pVRERETyV17e+XT27iWwZQumqwvn1CkCq1dDc3PC/e3atQufz8f999/f0zZz5kxuuOGGfscdP36c2267jZqaGmpqati3bx8Ap06d4rbbbmP+/PnMmzePPXv20NXVxbJly5g3bx433HADzz333EXjvvzyy9x1110A3HHHHezcuRNrVdREpFv3Mxljx8aeyYh9JX/c8eMtV14ZG7e11dDaamhpMckfWERERCSD5eWdT9++fbjTptH20EMEw2GCTzyBb+dOoosWJdTfu+++y6xZs4Y8btKkSbz00ksEg0EOHz7MAw88wI4dO9i2bRs33XQT3/zmN+nq6qKtrY2DBw/S0NDAa6+9BsDHH398UX8NDQ09d1Z9Ph8lJSV89NFHTJgwIaF5iOSadD2T8eyz2lJFRERE5EL5l3xaiwmFcKurwRgoLcWdOBGnqSnpQ0ciER577DEOHTqE4zgcPnwYgOuvv56VK1cSiUS4/fbbufbaa7niiis4duwYjz32GLfccgsLFy5MenwiIiIiIiLJkn/LbiMRiEaxfn9vm98PnYlvg3D11Vfz1ltvDXncmjVrKCsrY/fu3ezYsYNwOAzAjTfeyMsvv0xlZSXLly+ntraW8ePHs3v3bubPn8+GDRt45JFHLupv8uTJfPDBBwBEo1FCoRCXXnppwvMQERERERFJlvxLPv1+8PkwkT7L8SIRKCxMuMuamhrC4TDPP/98T9uhQ4fYs2dPv+NCoRDl5eU4jsPWrVvp6ooVPTlx4gRlZWXce++9fPWrX+XAgQN8+OGHuK7LHXfcwXe/+10OHDhw0bi33XYbtbW1APzjP/4jNTU1GKPnykREREREJPPk37JbY7AlJZjGRrAWmptxzpwhOmPGKLo0vPDCCzz++OM8/fTTBINBpk6dypNPPtnvuAcffJB77rmHrVu3cvPNNzNu3DgAdu/ezTPPPIPP56OoqIi1a9dy8uRJVqxYgevGtmhYtWrVRePec889PPzww1x33XVccsklPZV2RUREREREMk3+JZ9AdM4cAtu2MXb9evxtbVjHIbpgwaj6nDx5cr87n311Fw2aPn16v7uhP/jBDwBYvHgxixcvvui8Xbt2DTpmMBhk06ZNCUYsIiIiIiKSOnmZfLpz5xL2+7F79+JWVBBduhRKS9MdloiIiIiISM7Ky+QTY3Bnz6bjmmsIBoPpjkZEkuToUcOePT5CIUMwaKmpiTJ9uvWs3at4vDzHq1jTKRfmICIiIhfLv4JDIpI3amv9nDhh8Pvh1CnD+vWFHDliPGv3Kh4vz/Eq1nTKhTmIiIjIxXLizqfjOITDYQKBQLpDyTjhcBjH0TUGyU9Ll0aoqLA4DrS2wlNPBdm/3+dZe1VVZOgghhHPYP2M9ByvYk2nXJiDiIiIXCwnks+ioiJaWlpob28f0XmhUIiSkpIkRZUZHMehqKgo3WGIpEVlZe9SzbNnDZ2dUFJiPWv3Kh4vz/Eq1nTKhTmIiIjIxXIi+TTGUFxcPOLzGhsbmTJlShIiEpFM0tICmzcHKC62LFwY9bzdq3i8PMerWNMpF+YgIiIivbQeU0RyWmsrrFlTSHs7PPRQmO4aY161exWPl+d4FWs65cIcREREpD8lnyKSs9raYN26AM3NsGxZmPJy62m7V/F4eY5XsaZTLsxBRERELpYTy25FRAayaVOAhgaHigqX7dtjP+6qq10OHizwpH3+/C5P4hmsn5Gek8gYmSYX5iAiIiIXU/IpIjmrpMRSVeUC0N5uzn/3rt2reLw8x6tY0ykX5iAiIiIXU/IpIjlryZJ4W3PEu4M20vaRiR+Pd+ckMkamyYU5SJJZi/PGG/jq6rCFhURvvRU7eXK6oxIRkSHomU8RERHJKs7evQS2bAHXxTl1isDq1dDcnO6wRERkCEo+RUREJKv49u3DnTaN8LJldC5fjnFdfDt3pjssEREZgpJPERERyR7WYkIhbFkZGAOlpbgTJ+I0NaU7MhERGYKSTxEREckekQhEo1i/v7fN74fOzvTFJCIiw6LkU0RERLKH3w8+HybSpzBVJAKFhemLSUREhkXJp4iIiGQPY7AlJZjGRrAWmptxzpzBnTQp3ZGJiMgQtNWKiIiIZJXonDkEtm0jsHYtprkZ6zhEFyxId1giIjIEJZ8iIh47etSwZ4+PUMgQDFpqaqJMn249Pydb5PLcJD3cuXMJ+/346upwKyqILl0KpaXpDktERIagZbciIh6rrfVz4oTB74dTpwzr1xdy5Ijx/JxskctzkzQxBnf2bMJf/zqR++7DTp6c7ohERGQYdOdTRMRjS5dGqKiwOA60tsJTTwXZv99HVVXE03OyRS7PTURERIZPyaeIiMcqK3uXlJ49a+jshJKSwZeZJnJOtsjluYmIiMjwadmtiEiStLTA5s0BiostCxdGk3ZOtsjluWUrY8wUY8wrxph3jDF/MMb8ZbpjEhGR3KXkU0QkCVpbYc2aQtrb4aGHwgSDyTknW+Ty3LJcFPgra+2/B+YCK4wx/z7NMYmISI5S8iki4rG2Nli3LkBzMyxbFqa8fOglpomcky1yeW7ZzlrbYK39/fk/nwPeBS5Lb1QiIpKr9MyniIjHNm0K0NDgUFHhsn177MdsdbXL/Pldnp6TLXJ5brnEGHMFcB2wb6DX6+vrh9WWq/JprpBf89Vcc1M+zRUya77V1dVxX1PyKSLisZISS1WVC0B7uzn/3ftzskUuzy1XGGOKgF8B37DWhgY65sJfJurr6wf9BSOX5NNcIb/mq7nmpnyaK2TXfJV8ioh4bMmSkW8hksg52SKX55YLjDF+YonnL6y1/5DueERE0uXoUcOePT5CIUMwaKmpiTJ9uh4V8ZKe+RQREclTxhgD/Ax411r7d+mOR0QknWpr/Zw4YfD74dQpw/r1hRw5YtIdVk7RnU8REZH8dSNwD3DQGPPW+bZvW2tfTmNMIiJpsXRphIoKi+PEqrQ/9VSQ/ft9VFVpBY9XlHyKiIjkKWvtbkCX9UVEgMrK3iW2Z88aOjtjdQvEO0o+RUREJH9Yi/PGG/jq6rCFhURvvRU7eXK6oxKRDNLSAps3BygutixcGE13ODlFz3yKiIhI3nD27iWwZQu4Ls6pUwRWr4bm5nSHJSIZorUV1qwppL0dHnooTDCY7ohyi5JPERERyRu+fftwp00jvGwZncuXY1wX386d6Q5LRDJAWxusWxeguRmWLQtTXq4lt17TslsRkSHEK73uVXsiY2eabIlT8py1mFAIt7oajIHSUtyJE3GamtIdmYhkgE2bAjQ0OFRUuGzfHkuTqqtd5s/vSnNkuUN3PkVEhhCv9LpX7YmMnWmyJU7Jc5EIRKNYv7+3ze+Hzs70xSQiGaOkxFJV5TJ2LLS3m/Nf6Y4qt+jOp4jIEOKVXveqfbAS7tlS9j1b4pQMl+xiQH4/+HyYSJ9/l5EIFBd7N4aIZK0lS/SZlWy68ykiMoTKylhSBf1Lr3vVnsjYmSZb4pTMlvRiQMZgS0owjY1gLTQ345w5gztpkndjiIhIXEo+RUSGKV7pda/aExk702RLnJKZUlEMKDpnDs7x4wTWrqVwzRqs4xBdsMDTMUREZGBKPkVEhiFe6XWv2hMZO9NkS5ySoc4XA7JlZUktBuTOnUt48WJwHNyKCsKPPgqlpZ6OISIiA9MznyIiQ+hben3Fit7S6161JzJ2psmWOCWDpaoYkDG4s2cTnj3b235FRGRISj5FRIYQr/T6wYMFnrQPVsI9W8q+Z0ucksFUDEhEJOcp+RQRGUJ36XWIlV6PffeuPZGxM022xCkZ7MJiQKEQzpkzRGfMSHdkIiLiESWfIiJDiF96Pd5dvZG2JzJ2ZsmWOCWzRefMIbBtG4G1azHNzSoGJCKSY5R8ioiISEZw584l7Pfjq6vDraggunSpigGJiOQQJZ8iIiKSGVQMSEQkp2mrFREREREREUk6JZ8iIiIiIiKSdEo+RUREREREJOmUfIqIiIiIiEjSqeCQiGSNo0cNe/b4CIUMwaClpibK9Om2p/3w4TKmTPFf1B7v+AvbpZeX75HebxEREYEU3fk0xkwxxrxijHnHGPMHY8xfDnCMMcasNsa8Z4x52xhzfSpiE5HsUVvr58QJg98Pp04Z1q8v5MgR09Pu89kB2+Mdf2G79PLyPdL7LSIiIpC6O59R4K+stb83xhQDbxhjfm2tfafPMbcB1ee/5gBrz38XEQFg6dIIFRUWx4HWVnjqqSD79/t62g8fbqKycvxF7fGOv7C9qiqS7ilmDC/fI73fIiIiAim682mtbbDW/v78n88B7wKXXXDYHcBmG7MXGG+MmZyK+EQkO1RWxhIYgLNnDZ2dUFJiPWuXXl6+R3q/RUREBNLwzKcx5grgOmDfBS9dBvypz9/fP9/WMFA/9fX1nsTjVT/ZJB/nDPk571ydc1ubw89/Xk5XVwtTpzZQX2972n/603MDtsc7fqD2bJPM/85evkde9jXcOVdXVyc8hoiIiHgrpcmnMaYI+BXwDWttaDR9efELRX19fd79YpKPc4b8nHeuzrm1FZ57rpAxY2DlyjDl5Vf1tP/why2MGTPhovZ4xw/Unm2S+d/Zy/fIy75y9d+2iIhIrkvZVivGGD+xxPMX1tp/GOCQD4Apff5++fk2EREA2tpg3boAzc2wbFmY8nLbr72lpWDA9njHX9guvbx8j/R+i4iICKTozqcxxgA/A9611v5dnMP+H2ClMWYrsUJDzdbaAZfcikh+2rQpQEODQ0WFy/btsR9f1dUuBw8W0NDgUFISHbA93vEXts+f35WeiWWgeO91Iu+Rl32JiIhI9krVstsbgXuAg8aYt863fRuYCmCtXQe8DPxH4D2gDbgvRbGJSJYoKbFUVbkAtLeb899725ua3AHb4x1/Ybv08vI90vstWcNanDfewFdXhy0sJHrrrdjJqn0oIuKVlCSf1trdwKCbullrLbAiFfGISHZasiTe1hyxO2j19Y1UV5de1B7veIkv/nud3r5ERmWI5NLZu5fAtm24V12Fc+oUgdWr6fzWt9IYsIhIbknZM58iIiIi6eTs3UtgyxZw3Z7kkubmntd9+/bhTptGeNkyOpcvx7guvp070xixiEhuUfIpIiIieWHQ5NJaTCiELSsDY6C0FHfiRJympvQGLSKSQ5R8ioiISO4bKrmMRCAaxfr9vef4/dDZmZ54RURykJJPERERyX1DJZd+P/h8mEik/zmFhamNU0Qkh6Wq2q2IiIhI+sRLLouLY382BltSgmlsBGshFMI5c4bojBkX96WquCIiCdGdTxEREcl9FyaXzc04Z87gTprUc0h0zhyc48cJrF1L4Zo1WMchumDBRV0NVbhIREQGpuRTRERE8sJQyaU7dy7hxYvBcXArKgg/+iiUll7Uj6riiogkRstuRSTrHT1q2LPHx+HDZUyZ4qemJsr06ZZXX3V48cUAZ88axoyxLF4c5rOfdYfsJxQyBIO2p59skgtzEEkWd+5cwn4/vro63IoKokuX9k8ujcGdPZvw7NnxOzlfuMitrlZVXBGREdKdTxHJerW1fk6cMPh8llOnDOvXF3LkiOHJJ4McOWLw++HkScP3vjeGnTvj/9jr7sfvp18/2SQX5iCSNN3J5de/TuS++xJ7TlNVcUVEEqY7nyKS9ZYujVBRYTl8uInKyvE89VSQ/ft9/OhHHcyc6eLzQVMTfPnL43jppQALFnQM2o/jQGsrPf1UVUUGPD4T5cIcRDLaUIWLREQkLt35FJGsV1kZS7YAzp41dHZCSYnluutiiSfAsWMO4TBMmBB/CWq8frJJLsxBJKMNo3CRiIgMTHc+RSRntLU5bNsWoLjYsnBhtKf99GpwtCwAACAASURBVGlYtSpIUZHlkUeGXhrX0gKbN1/cTzbJhTmIZKronDkEtm0jsHYtprk5blVcERHpT8mniOSE1lbYsqWcQABWrgwTDMbam5rga18bS3s7PPNMB+PHD93PmjWFtLf37yeb5MIcRDLZkIWLRERkQEo+RSTrtbXBunUBWloK+M53wpSXx5aZnjkTSzybm+Hppzu45pr4lW779tPcDCtW9PaTTXJhDiIZbzhVcUVE5CJKPkUk623aFKChwaGkJMr27bEfa9XVLuvWBTh92lBe7rJmTQCA667rYvnygYvvdPdTUeH262f+/K7UTMQDuTAHERERyU1KPkUk65WUWKqqXJqaXNrbY9uKtLfD+PGWK6+M3e1sbY21t7TE33aku5/Y+b39ZJNcmIOIiIjkJiWfIpL1liyJ3cmsr2+kurr3uavPfW5kd/u6+8lmuTAHERERyU3aakVERERERESSTsmniIiIiIiIJJ2STxEREREREUk6JZ8iIiIiIiKSdEo+RUREREREJOmUfIqIiIiIiEjSaasVEcko//Cbk/zsN/v4OHwWv89h1W3389nPxvatfPVVhxdfDHD2rGHMGMvixeGe1wZy9Khhzx4foZAhGLTU1ESZPt16Fmu8eBIZd6TndB9/+HAZU6b4E55bst8jkYxmLc4bb+Crq8MWFhK99Vbs5MnpjkpEJGfpzqeIZJQnt7zBsbZ3CBe+T1NHA9/73hh27oz9qHryySBHjhj8fjh50vR7bSC1tX5OnIgdf+qUYf36Qo4cMd7FGieeRMYd6Tndx/t8dlRzS/Z7JJJS1uLU1RFYtw7/xo2YhoZBD3f27iWwZQu4Ls6pUwRWr4bm5hQFKyKSf3TnU0QyynMrbmPWtbez9Y8v8E7DMfYdhJdeCrBgQQc/+lEHM2e6+HzQ1ARf/vK4ntcGsnRphIoKi+NAays89VSQ/ft9VFVFPIk1Xjx//dedIx53pLF2H3/4cBOVleMTnluy3yORlOi+g7ltG76DB4nW1OBYS2D1ajq/9S0oLR3wNN++fbjTphFetgxCIYJPPIFv506iixaleAIiIvlBdz5FJKN8+s99BPyxH03NHxvCYZgwIbYM9LrrYokewLFjTr/XBlJZGUuqAM6eNXR2QkmJd0tK48WTyLgjPceruSX7PRJJhe47mAXHjkEggGlupvPuuzGui2/nzoFPshYTCmHLysAYKC3FnTgRp6kppbGLiOQT3fkUkYwUaoZdr/q4vMjyyCOd/V47fRpWrQpSNMBrA2lpgc2bAxQXWxYujHoea7x4Ehl3pOe0tTls2zb6uSX7PRJJJt++fbhTp2JKS3Evv5yCf/s3fG++OXgyGYlANIr1+3vb/H7oHPpnioiIJEZ3PkUk4zQ1waZNAaIR+MlPOhg/vv9rX/vaWNrbL35tIK2tsGZNIe3t8NBDYYJB72MdKJ5Exh3pOa2tsGVL+ajnluz3SCSpuu9gTpgAXV3YvncwB0sm/X7w+TCRPkvMIxEoLExN3CIieUh3PkUko5w5cz6ZK4ab/3OUa65xL3qtuRmefrqj32sDaWuDdesCNDfDihVhysu9XU4aL55Exh3pOd3Ht7QU8J3vJD63ZL9HIknXfQdzzJjeZLI76YxEoLh44POMwZaUYBobwVoIhXDOnCE6Y0Zq4xfJE6quLqDkU0QyzIN/3cC7xb+mcMJJ6t60LNy7mWumXsbqR27iG98Yw+nThvJylzVrAgBcd10Xy5cPXBxn06YADQ0OFRUu27fHftxVV7vMn9/lSazx4jHGjHjckcbafXxJSXRUc0v2eySSdH3uYPYkk+Ew+HxDJpPROXMIbNtGYO1aTHMz1nGILliQwuBF8kdtrR9jYNKk3urqDz/cSVWVdwmoV9uQSfIo+RSRjFJcGuYS04qxE6AV2umkpSO2bG78eMuVV8buLra2xrYDaWmJvy1ISYmlqip2fHu7Of/du1jjxXPZZSMfd6Sxdh/f1OSOam7Jfo9Ekq7PHczopz9N4Be/wDlyBLe8HDt5cvxk0lrw+bBjxlDw+ut0/dmfEVm6NG5lXBEZnVRUV+9OcPtuQ+Z1giujo+RTRDLKz/9+GrBswNeefXbgLVXiWbIkuduFjDSewYw01u7j6+sbqa5O/JflZL9HIqnQfQfT5/NhHQcch65Pf5roX/xF3GTS2buXwLZtuFddhVtUhDl9Gjt2bIojF8kflZW9CWCyqqt7tQ2ZJI+STxEREclq7ty5hP1+fHV1uNdfT/jb38ZOnjzoOdrjU2R0En2GM1519dE8E3rhuVOnFjJmjLYPy0RKPkVERCS7GYM7ezbh2bPjH2Mtzhtv4KurwxYWYj74APf667XHp0iCEnmGs2919ZUr+1dXH80zoRee++qrZUyYEKCkRNuHZRolnyIiInnKGLMB+ALQaK29Jt3xJFPfZbbOBx/ge/11OmfO7D1Ae3yKjMhIn+Ecqrr6aJ4J7XtuUxN86UtjCIUKeO65dm0flmGUfIqIiOSv54Fngc1pjiPp+i2zbW7G95vf4Dt0iJ57IoNtyyIiFxnpM5xDVVcfzTOh3ee2tcEzzxTS3t7Ff/7P2j4sEznpDkBERETSw1q7C/go3XEknbWYUAhbVhZbZjt+PO4ll2BOnoxVvW1uxjlzBnfSpHRHKpJ14j3DeaHu6upjx8aqq8e+Eu9vIOvXB9i1q4CioihtbYaNG/3s3l0w0ilJEunOp4iIiAypvr5+WG2ZyEQiVDY20nbppZw9H/OUSy5h7Hvv0fzDH1Jw7hztLS2cvPxy3Dhzypa5eiWf5qu5Jq693eEXvyintbWNJUtO86c/xU8WP/3peDEl1t9AsezYcSWO4zB7dgsNDbE7qsZ0UF4eGnY/2SqT/h1XV1fHfU3Jp4h4YjRV6kbr/XPv89r7r/HHP/2R8tZyvjbraykZVySfXPjLRH19/aC/YGQUayksK6P00kspa27GV1dHQXs77qc+xaTycuzUqURvvZXpcSrkZtVcPZBP89VcE9fWBmvXBvD5DN/+dpjKyivT1l/3uZ/8pGHFijCtrQ195loKlI8qtkyXTf+OlXyKiCdGU6VutPZ+sJd3PnyHMx1ncFvdpI8nIlnGGGxJCc7rr1Pw+uu4l1+O09iIdV06v//9uHuBikh8Qz3Dmcr+Ljy3sXEiVVU+wmGTloviEp+e+RQRTyxdGuFv/ibMgw+GefTRMIEA7N+fmutbi6oX8b0bv8e0cdNSMp6IZJ/onDn4Dh7ENDRgOjuJ/tmfweTJ+HbuTHdoIllpuM9wpqK/C8/t6HD4l38p4MQJg9/fe1H8yBGTeIDiCd35FBFPjKZK3WgFCgIpGUck1xhjaoHPABONMe8Dq6y1P0tvVMnhzplDV3U1JhrFnTyZ6H334X/hBe3tKZKgJUuG3gIlVf1deG59fSPjxo1PeOsWSR4lnyLiqdFUqROR1LLW3pXuGFImGsVOmkT02muJfulLsTbt7SmSNUZaW6KzM/ZIUCgUu4N6+jTMm6dlt+mm5FNEPNPaCmvWFNLeDitXhrWxs4hkDr8ffD5MpM9dD+3tKXmsu1jfR50f4eBkfLG+kdaW6D6+uBh++1sfra2GK6/M7ItN6SzemCpKPkXEE21tsG5dgOZmWLFCGzuLSIY5X3TINDbG9vYMhXDOnCE6Y0a6I5MclenJXXexvi7bhc/J/JRg6dLIiJbRLl0aobjYsnZtIddc00UkYjhwwMfVV2fustt0Fm9Mlcz/lyYiWcHrqncjcfTjo/z22G852X6SokARGw5s4LLiy7il6pakjy0i2SM6Zw6BbdsIrF2LaW7GOg7RBQvSHZbkqExP7hZVL+KLM77IC394gWPNx9IdzpBGWlti/HjL2rWxi+Jf/nKEX/4ykLJaFIkaaYKdjVTtVkQ84XXVu5GIuBHaom2U+kupGFdBR1cHHV0dqRlcRLKGO3cu4cWLwXFwKyoIP/qotlmRpOmuxH7VJVelO5QBBQoCOCb7UoHh1pbovig+dqzlv/7XIEeOOASDmZ18VlbGEk9IffHGVMm8yzAikpW8rno3Ep+49BN84tJPZNUmyyKSBsbgzp5NePbsdEcieUCV2L03ktoSJSWWyy5z+d3vfHR2wvz5UbqSvxhrxAZ6zrO83OZs8UYlnyIiIiIiktFGWlviL/4iwtq1AWbM6GLFinC/ZbuZ5MLnPJ97rpDCQovPl5vFG5V8ioiIiIhksEwvXpQKI60tkc5aFCPR9znPpia4556xFBXB6tXtOVm8UcmniIiIiEgGS0bxou5ifQ2tDUTdaMYX6+uuLQGx2hKx794dny5978j+j/8RIBQyTJ3aldEJ82go+RQRERER8ZiXyV0yKtN2F+ubMGYCQMYX6xtpbYl01qJIREsLvP12AWVllk9+0s3ohHk0lHyKiIiIiHjMy+QuGcWLuov1Sfp1F1K66iqXlStze690JZ8iIiIiIh5TcifDMdJCStlOyaeIeGKkxRBSUTwhmwo0DFRqffr03P4AEhERyXfZUhjJK0o+RcQTIy2GkIziCekYwysXllpfv76Qhx/upKpKCaiIiGSedF80Tff4XsmWwkheyezfxkQka4y0GEIyiiekYwyv9C213toKTz0VZP9+H1VV2VUwQUREvJeJlWkTuWg62oSx7/mvvFLAtGkuM2bYrL5om22FkUZLyaeIeGKkxRCSUTwhHWN4pW+p9bNnDZ2dsauhIpIG1uK88Qa+ujpsYSG+T3wCqqvTHZXksUysTJvIRdPRrvLpe/5VV3URjTosXBh7TlIXbbODkk8RkQzS0gKbNwcoLrYsXBhNdzgime+CRDF6663YyZNH1aWzdy+Bbdtwr7oK59QpKvbtg2uugdJSj4IWGZlMLF6UyEXT0a7yiXf+/PlRXbTNEk66AxARkZjuUuvt7fDQQ2GCwXRHJJL5nL17CWzZAq6Lc+oUgdWrobl5VH369u3DnTaN8LJldC5fjrEW386dHkUskltGctG0sjKWOEJiq3wGOt/vt7pom0WGnXwaY4qMMZcbY4qSGZCISD7qW2p92bLcL7Uu4pWLEkXXHV2iaC0mFMKWlYExUFpK5JJLcJqavAtaJEcketF0tKt8us8vLLT88Y8FumibRQZddmuMuQZ4GLgdmAYYwBpjjgH/DPzUWnsw2UGKSOYbaTGEVBRPyMQCDfHkW6l1EU+cTxTd6uqeRNGdOHF0iWIkAtEo1u/vHcbng85ODwIWyR5DbVeW6P6UfRPWlStHnjB2nx8KQTAY+3vf8Y8eNfzv/z2BMWMCWV0FN1fFTT6NMVuBfw9sBe4G3gXOAcXA1cAC4BfGmHestV9JQawiksFGWgwhFcUTMrFAQzz5VmpdZNgGe6ZzgEQRv390iaLfDz4fJtL7DJqJRqGwMPE+RUYhXXtWD7VdWSIXTRNNWAc6f/x4y5kzF4+/a1cBZ88GmDFDW5dlosHufL5grf2nAdrPAnvOfz1pjPlCUiITkawy0mIIqSiekIkFGuLJt1LrIsN1YfGfwOrVdH7rW7HiPwMkikQiUFyc+IDGYEtKMI2NYC2EQvjPnsWdNGn0kxFJQLr2rB5qu7JELpqOdpVP3/MPH3bOj+swdmx3HLGiROfONTBjRpG2LstAcf8Fx0k8Ez5OREREZKT6PtNJKETwiSfw7dxJdNGiARNF58wZojNmjGrM6Jw5BLZtI7B2Laa5mXZjiC5Y4NGMREYmXXtWD7VdWSIXTUe7yqfv+TNnxr5XV3fxuc/1T17r62PftXVZ5hn25RNjzH8ArgP6FRyy1j7hdVAiIiIiw3mm88JE0TrOqBNFd+5cwn4/vro63IoKTtXUcKW2WZE0yaY9q4cy2lU+N9wQZc8eH6GQGfJ5zuEWNTp61Ay7Txm9YSWfxphngC8DrwJ9r0/ov4yIiIgkxzCe6bwwUYwuXTr6/TiNwZ09m/Ds2QBEu2+jiEha1db6MQYmTRr8ec72dmfYRY2G26d4Y7h3PpcA11hrTyYzGBEREZEew3mm84JEcVgGK2IkksPSVbzIK0uXRqioiO31Ge95zrY2qK0tw3GGV9RoOH2Kd4abfP4JUI1xERERSZ0kPdM5aBEjkSw03KRyJMWLMnG7ssrK3kQy3vOcmzYFaGry8+/+nR1WUaPh9CneGW7y+QDw340xtcDpvi9Ya3d5HpWIiIgIyXmmc9AiRiIZZjhJ4HCTypEUL8rk7coGe56zpMQyZUonY8eOrKjRcJ8RldEZbvL558BtQA0XP/M51eugRERE8pkxZhzwY+BG4A/A96y1h/u8ftBaOzNd8aXSqJ/pvHCJ7ec/j/PHP2KiUfzPP0/01lsvKmIkkkmGkwQON6kcSfGiTN2urLWVQZ/nXLIkQn19I9XVw/85MVSf4p3hJp9PAP/JWvubZAYjIqnn1fMfq19fzdo319ISacEYw4nlJwbtP5Fx6xrqqH2nlqb2Jhwcnv/C80BvpbrDh8uYMsXfU6lOFewki/09cDnwt8ACYL8x5r9Ya3ecf/2K9ISVBok809nHhUtsCx9/nIL33sOdObNnya0tLe1XxGhAek5U0mQ4SWAuVcQdTFsbrFsXoLl5eM9zpqtPic8Z5nGtQMLLa40xG4wxjcaYQ3Fe/4wxptkY89b5r+8nOpaIjEz3Up0Pzn1AQ2tDwv389K2fcqbjDO3RdtqjvQsk4vWfyLi/fPeXvN30Nu+H3ueDlg962mtr/Zw4YfD5bE+luiNHTE+730+/dpEs8AXgLmvtS9babwBfAV40xvzHNMeVOGtx6uoIrFuHf+NGTEPiP29Gou8S287ly3EaGsBaogsW0Ll8OcZ1Kaivh8LCQftx9u4lsGULuG5P0kpzc0rmICIxmzYFaGhwuOSS2POcGzf62b27IOP6lPiGm3x+H3jaGFNhjHH6fg3z/OeBW4c45lVr7azzXz8cZr8iMkqLqhfxvRu/x1WXXDWqfv75y//M8WXHKS3sv8wlXv+JjLtq/ir++cv/zIwJ/YuNLF0a4W/+Jsyddzbx6KNhAgHYv9/X0/7gg+F+7SJZYAx9Cv1Za38NLAI2GGPuJAu3OktL8ua6OH/8IwWHDuF//nlMa2usgq7fj2lqgpIS3KIizOnTuJMmDdrVhUmscV18O3cmN34R6aekxFJV5fY8zxn7yrw+Jb7h/ha24fz3h/u0GWIffkNeGrDW7jLGXDGiyEQkJbxaqjO1dODHv+P1n8i4RYGiAdvjVapTBTvJYoeI1Vn4f7sbrLV7jTG3Af8MjE1XYIlKR5Ef53e/o+DgQbo++clYwvv00xCJYCdOxDl+nMDatfjq6sB1By9iZC0mFMKtrgZjoLRUz4lKVsrECrYjsWSJ99ufJKNPiW+4yeeVSY0iZp4x5gBwEvimtfYPgx1c79GGz171k03ycc6Qn/MeyZwbTzfyUdtHo36fotEorute1E+8/hMZN9QcoqOj46Jz2tocfvrTc3R1tTB1agP19ban/ec/L7+oPVfo33Z81dXVSY4kaZ4ELr2w0Vr7pjHmZuD/Tn1Io5Cm5M1XV4e95BK6FiwgcvvtBJ94IhZHVRXhxYtjr48dS9enPjVoESMTjUI0ivX7exv9/t7nRPU8qKTZcJNKryrYprumQrLHT/f8Mj2e0Rhu8nkKcK21PZcGjDF+hr9sdyi/B6ZZa1vOP8/yv4BBf2Pw4heK+vr6bP7FJCH5OGfIz3mPdM5lnWW0NbeN+n3y/daH4zoX9ROv/0TGLTlSQtAN9juntRV++MMWxoyZwMqVYcrLr+ppf+65QsaMoV97rtC/7dxkrX15kNcOAfenMJzRi0QGT96SwVrMuXPYCRN6l9gWF+Pr6MBai/vnf064uprg0aO4Vw3+c8H6fODzYSKR3r6PHcM5exb/mDG4kybh/9d/1b6hkjbDTSq9qmBbW+vHGJg0qbemwsMPd1JVlZqEKNnjp3t+mR7PaAw3+fw18NfA3j5tfw48BXxmtEFYa0N9/vyyMWaNMWaitfbMaPsWkdzXXamupaWA73ynt1KdKtiJZAi/v3/yBrGEtLg4eWOeT3i7ZszoXWL7+uu4xcUQCIxs31BjsCUlmMbG2F3O3/4W3/79dM2ahXPqFP5f/pLoZz6jfUMlaYaqEJ/qbVGWLo1QUWFxnNhF3qeeCrJ/v4+qquQvYT161FBaGltEUVBg+cIXorz4YqGn46dzftkQz2gMN/mcCey7oG0/8GdeBGGMqQBOW2utMebTxO6ofuhF3yIyOK+e//jBqz9gw9sbaOtqw3VdrlhzBRVFFdQuqh2w/09c+okRj7vrxC42vr2Rk60n6bJdPPDyA1x1yVWMf2sVDQ0OJSVRtm+P/VirrnY5eLCAhgaHigq3X/v8+V2Jv2EiMnIXJG+EQjhnzhCdMWPocxN1PuG106YR/tznepfY1tQQXbBgxPuGRufMIbBtWyyJ3b4dW1JCxxNPADBu+3bMRx/peVBJmu4K8V22C5+T/sJ56aypcOFdwHXrColGYd4878bPtJoRmRbPaAz3X28zUE5s+W23cmJbsAzJGFNL7A7pRGPM+8AqwA9grV0HfAlYZoyJAu3AV6y12fmOimQZr57/aIm0EHEjBJwAOLF+OyIdcftPZNyOrg5ao60957RH22kNtzL1fKW6piaX9vbYVirt7b0V7GJ/720XkdTrm7wN+47jaHQnvE1NuHfd1bvEtro6oX1D3blzCfv9+F5/HevzEb31Vhg/HsJhbGEhTltb78HJXlIseWdR9SK+OOOLvPCHFzjWfCzd4fRoaYHNmwMUF1sWLoymZMy+dwEbG2HJknFMnOgmZfx0zC+b4knEcJPPXwFbjDGPAkeA6cDfAS8O52Rr7V1DvP4s8OwwYxERD3m1VOfHC3/Mjxf+OO4YI2mP5/NXfp7PX/n5AV6JLTupr2+kurrvHQzd4ZTsZoy501q7bYD2L1lr/2c6YkpUT/I2wjuOo+FpwmtMLGm99loKGxqw3VuznN+6hY4+F8+SvaRY8o5Xlem91NoKa9YU0t4eq6kQDKZm3O67gK2tsHp1IZ2d8J/+U8Tz8dM1v2yJJ1HDTT6/A/yE2FLbQqCD2PYrjycpLhEREYGfARcln8B6IKuSz57kbYR3HEcjKQnvhc+vGhMrpNTenrolxSJpVt/4Pj/6+X4+7PyIBTdbysu/NvRJHmprg//23wLs3VvAvHlRvvhFb+8CZlrNiEyLZzSGlXxaazuAFcaYlcBE4IyWxYqIiCSHMabq/B8dY8yVxPbW7lZF7CKwDCUZCe8Az69SXAwtLalbUiySZj95sY4jre8ydlyUvX8oYOMRf0prKqxfH+A3v/ERCEBlpUttrbfjb9oUyKiaEZkWz2jETT6NMeXW2tN9284nnE1DHSciIiKj8h5giSWdhy947RTwt6kOKCcluD/nhct53cpKIrfdhu/dd1O2pFgkneaNv4NrzJd4o2szH0WP0t5uUlZToa0N6uoKGDMG5s+P4vfHxvZy/EyrGZFp8YzGYHc+/9UYsxP4ObDPWut2v2CMcYBPA18FaoBrkhqliIhIHrHWOgDGmJ3WWt1CSxJn714C27aNeH/OgZbz2smTCetupySJV5XpvXLv3QaIsvlQlGPNXSy/MZyysf/+7wM0NxvGjbMcOeJQXe1yww1dnt4FXLIks7YwybR4RmOw5PM64CHgvwNXGmOOAOeAYuBKYldlfwp8I9lBioiI5CMlnsnl27cPd9q0ke/PmYbnVyW/eVWZPhccOlSA328pLIQPPzS8/76PigqX+fPTHZkMR9zk01obJlaB9lljzBRie32OB84Cb1trP0hNiCIiIvnp/POePwJmAUV9X7PWTk1LULnAWpy6Ogp27MBOnow5dQo7ebL255SM5VVl+lQ7etSwZ4+PUMgQDFpqaqJMnz66sjE/+UlHz1Yrra3w1FNBAgEz9ImSEYZbcOhPwJ+SHIuIDNP7597ntfdf46POj3Bw+Nqs1FWZq2uoo/adWpram3BweP4Lzw96fLxY4/WTzrl5JRkftpK3thB75vOvgLYhjpVhcvbuJbB1K3R1YVpaepfban9OyVKZ9NnZ9zPwlVcKmDbNZcYMy6lThvXrC3n44U6qqhL/TOzeagXg7FlDZ2fsmchU0ud84oa71YqIZJC9H+zlnQ/foct24XNS+7/xL9/9JW83vU2X20WBUzDk8fFijddPOufmldpaP8bApEl49mEreeuTwI196y7I6Pn27cO98krMpZfiXn45Bf/2b/h27oztz1lUhFNX168IkUimS/Vn52DPoPb9DLzqqi6iUYeFC2Pbgzz1VJD9+31UVY3+GcaWFti8OUBxsWXhQm+3WhmKPucTl52/2YnkuUXVi/jijC/ywh9e4FjzsZSOvWr+KoK+II/8+hHeO/vekMfHizVeP+mcm1eWLo1ctCTIqw9byTu7iNVgeCPdgWS97sq2r79Owauv0nXzzdjSUkxLC+6ECTgnTuCcOUOXMQS2bOlXhMi58850Ry8yqFR/dg72DGq8z8D586Oe3aVsbYU1awppb4eVK8MEg6PuckT0OZ84JZ8iWShQEEjb2EWBoqEP6iNerPH6SefcvJIJS4IkZxwD/sUY8xKxLVZ6WGu/n5aIslRPZdsrrsA5dw5++1s6v/51Ai+/jPngA4hEcGfMwESjFxUhKn79dbj++nRPQSSuVH92DvYM6kCfgX6/9ewuZVsbrFsXoLkZVqyI3VFNNX3OJ85JdwAiIrkqnUuCJGeMA/4J8ANTLviSEeipbLtyJdFPfQoTjeK0thJevBishUsvJfzII5hwGFtWBsZAaSnuxIn4P/oo3eGLZJ3uz8DCQssf/1hAezs89NDo71Ju2hSgocHhkkss27f72LjRz+7dQz8GlAz6nB+5Yd35NMZcCnyTgavt1SQhLhGRrJbuJUGSG6y196U7hpxgLSYUwq2uBsfBTpqENQanqYnoZz6DvfJKojfeiJ04EaJRrN/fBTsG9QAAIABJREFUe67fj9OmWk8iI9H9GRgKQTAY+7tXdylLSixVVbHH4Nvbzfnvo+52xPQ5n5jhLrvdAhQCL6JqeyIig8qEJUGSO4wx/w64Eyi31q40xswACq21b6c5tOwRifRLKqNz5uD/p3+CV16hsKkJ6zhEFyyIVbv1+TCRSL9z3UD2Pw4gkip9PwPHj7ecOeNQUeGyfXss7aiudpk/vyvh/pcsSf9zlfqcT9xwk88bgEnWWtUfF8kAg1WZS7ZdJ3ax8e2NnGw9SZft4oGXH+CqS67i8XmPjyjWMb4xA/bzlau/kra5eaV7SZCXH7aSn4wxdwJrgF8Bi4GVQDHwFHBzGkPLLhckle7cuUSvuQbn7FncigqiS5dCaSkAtqQE09gYW4obCuGcOUOkqiqNwYsMLZ2/F1yo72fg4cPO+buTDmPHdt+tTHlIntPnfOKGm3y+DVxObK8xEUmzwarMJVtHVwet0daesduj7bSGW0ccqzFmwH7SOTevZMqSIMkJPwRuttYeMMb8X+fbDgB/lsaYso8x2OJinN//nsDHH2OtxUQiRP7LfyG6aFG/Q6Nz5hDYto3A2rWY5mas43DuU5+iLE2hiwxHJn129v0MnDkz9r26uovPfS53EjN9zicubvJpjLm/z1//lVi1vY1cXG1vQ5JiE5E4Bqsyl2yfv/LzfP7Kzw/7+MFijddPuubmlUxYEiQ5o4zYBWAA2+e71njF072lSp99Ou3kybjFxQR37aLr5EnMxx9jzp2j41vfuuh0d+5cwn4/vrq6nruibktLGiYiMnzp/L3gQvnwGZgPc0yWwe583nPB398HPndBmwWUfIqIiCTHG8Q+jzf3afsKsD894WS+ni1V+uzT2fmtb+GcO0fkP/wH7PTpWGspeOcdfG++SXTKBYWDjcGdPZvw7Nm9bfX1qZ2EiEiOipt8Wms/m8pARETk/2fv3uOrqu78/7/WzrkkQBINtwAqJZCkOqOlHabQwkBLp4K9xBlmevHSFnVQudRpp9OZ6oh+63yrTvttfzO2BYotKqWgzfzGaTtVab9asRYCDa03sDZquAQSEm4n5H7O2ev7x0lIgFzOSc7JPid5Px+PPEjWXmftz5bgPp+z1l4fkQvcAfzCGHMLMNYYsx0oAeJffjDKnC2p0qNOp+/55zFnzuD+2Z8Rvu46AMw3voHT0OBxtCIio0u8pVZ+b619dy/tldbaOb29RkRERIbGWvuHzt1uP0as3udh4H+stVoH2pueJVV61Ol06up6LaFCexL3Uexjua+IiHRz4uw36/wGY4wBtP2biIhICllrW6y1P7bWfsNa+7gSz36cV1IFiCWZkUivJVQIBpN2aqeigsDWreC6Z5f7EgolbXwRkZGg35lPY0zXMyaBHt93eQewLxVBiYiICBhjZgBfA2YD43oes9Ze5klQ6ayPOp3k5vZaQiVSWpq0U/e63HfHjgt20xXxQnW14Wc/G09OToDsbMvChRFmztS+ZTL8Blp2+1Yf31vgN0B50iMSERGRLluJ3X+/BLR4HEv66GuJqzF9JpnuFVdcUEIlsmhR0uLpdbmvnimVNLFtm59TpwKUlkJdnWHjxiC33dZOUZESUBle/Saf1tqvAhhjKqy124cnJJGRq+ZMDbtqdnGy/SQODitmr0j6OZ568ym+99L3OHr6KDm7c3jhxhcAqKytZNv+bTS0NuDg8OjHHh1Ue1/XkGi7iMTlT4D51lrX60DSSV872pKf32udzsiiRZCXd0EJFfLzkxNQX8t9k/lMqUicervvLl8e5syZWkpLx9HcDA8+mM2ePT6KilQyRIZXf3U+F/f4MXzez2dZa59LelQiI1TFkQr2n9hP1EbxOXHt95Wwh19+mDdOvUE4HMZvu98IPfH6E7zS8ApRN0qWkzXo9r6uIdF2EYnLC8C7iZVckU79LXHtrU5nV5J5QQmVZOlnua9IvJL1Ye1PX9rN8/v/QGu7S9CXxeJcw8yZ9mzFoFOnDO3tkJenWU+Jqa427Nzpo7HRpHxZdn/vBH/Q43sLXNL55wlgPGCI1f7UpkMicSorLmNZ6TK27NvCgdCBlJzjBx/5AeMC4/jQ5g/REOle8nXvgnvJ9mXz+V9+njdPvTno9r6uIdF2EYnLAeAZY8yTQF3PA9baezyJyGsDLXHtrU5nj9emZEfafpb7isQrWR/Wnvj13zDbfIrq/M0cbq4+u8QWoKkJNm8OkJtrWbw4kqzQJcNt2+bHGJg4MfXLsvur8zmj63tjzF3EEs611toWY8wY4D5iiaiIxCmQFUj5OQpyCnptHxcYl5T2vq4h0XYRictYYiVW/MClPdqT9o7AGLMU+A8gC/i+tfbBZI2dEkNY4trfct2h6nO5r0ickvVh7S3LDYWFEbbsj1DVEIXnYM8eH1dc4bBuXZDWVlizpoPs7OTFLplt+fIwhYUWxyHly7Lj/Vjli8BUa20YYtu+G2PuBI4CDyQ9KhEREcFae1MqxzfGZAHfBT5MbDXTb40xP7XW7k/leYdkCEtcU7kjbX/LfUXikawPa6dOjX02dbzB8PLLWYQrHUIh2LFjCgUFsHp1B5Mna8mtdOv6nYHUL8uON/lsBt5LbIfbLn+Odt4TERFJKWNMMXAdMA04Amyz1lYlafj3Am9aa9/uPNfjwLVA+iafg13imuodaftb7ivigZ07s3jrmMM7x1gOHTLU1ORx9dWwfXvs7X9xscuCBVGPo5R0MhzLsuNNPtcSe+bkZ8BhYkt/PgasTklUIiIigjHm48CPiC29PQiUApXGmM9Ya3+ahFNMI3Zf71IDzO2tY1XVhflub23DYey0aYx/5hna7ruPrDNnaG1q4ugll+D2E48Jh5laX09LQQGnOvtNDoWwLS3Ux3EdXl2rV0bT9abbtdYfq+dky8khxdXa6tDU1MJFF7Xyj9ftZ8eOfH75y/G89VYrwWBs7siYNiZPbkxW2NTUBPj973NpasoiGHSZM+cMl13m3Y7P6fb3mmpDvd7WVocf/Wgyzc0t3HDDMQ4fHnzyWVxc3OexuJJPa+0PjTF7gb8BpgJ/AP53Wi/LEUlD1aerefbAs9Q21xJxI2x6eRPTcqexpGhJ0s7x4/0/5ru/+y7H2o5hsgwf2PoBZl40k8/96ed45JVHONp8lKiNcstTtzDr4lnMnzY/ofZPX/7pXq+hpKAkofZkXrPICHY/cK219lddDcaYDwDfAZKRfMbt/DcTVVVV/b7BSKlZs3BmzCC3shJ72WVEli5l5kAbB1lLcNIk8sePZ0Jn3IH8fCgoIH+A6/D0Wj0wmq431dc6mB1sJ7VPoiXUQnFx8aBev7+umgd++jzhcQ3MfY/h9TG/4tIPXcLMtxfyV381jo98pOt56Xxg8tAusIfy8gDGwNSp0NAAv/jFFM9qiY6m32EY+vW2tMD69QF8PsNdd3UwdeqMgV80SHFvpdWZaCrZFBmCsBumJdLC+JzxALRF22iLtiX1HM2RZloiLYzzjSMYDNIeaac53ExbtI3mSPPZc7dGWmnuSLy9r2tItF1E4nIJ8Ovz2l7sbE+GI5y7kdElnW3pbTBLXLUjrXggkR1se/uA+kDoAFlOVkI74Jb//4bjZ1q5OLeAkwfh2TfDnD4YoWRsNOGllIkkv8O5aY0k12OPBaitdSgsdFO+LLu/Op8brbW3dn7/Q/rYWc9a+9mkRyUyQpUUlFBSUJLSc9x01U3cdNVNvX4KdvWMq3t9TaLtfV1Dou0iMqCXgC8B/9aj7R8625Pht0CxMWYGsaTz08D1SRo77WhHWhluiexg29uHte+a/C7+uuSvE9oBd9ZFxUw0XwCgvRV+8xsf48dYPrlyP9nZic1oJZI8D+emNZJceXmWoiIXgNZW0/lnas7V329RdY/v3+yzl4iIiKTKSuBnxpi/J/Zs5mXENgH8eDIGt9ZGjDFrgO3ESq1sstbuS8bY6Ug70spwS2QH22R9QH3DDbGZxq6llKWlUVav7qC5OfFn+AZT/kW1RDNP1+/McOivzucDPb7/6vCEIyIiIl2stX8wxlwOzCO258JRYHdX6bMkneMp4KlkjZfWtCOtjCLnL6Wsr5/A/PlZCS2lTLT8S3MzqiUq/Ypr8bgx5r+AHcAOa22ylvqIiIjIwOx5X6634YhIJjh/KWVbm5OypZQQm2ndsCFAKKRaotK3eDcc+h9gEfBFY0wesc0OdgAvWGt/m6rgRERERjNjzFXAfwNBYs9kXgK0GWP+2lr7sqfBiUhaO38pZVVVPcXFqVtmPpyb1kjmirfUyiZgE4AxZjpwK3APMI7YMyIiIiKSfJuA7wLfstZaY4wBvtjZ/meeRiYiAxqoxNpAu8kOR4m2ZBnOTWskc8W77PZyYCGx2c8FQB3wPWKznyIiIpIaJcC/W2stQGcC+h/A//I0KhGJy0DlxgbaTdbrcmWJJL/DsWlNdbVh504fjY2G7GzLwoURZs7U8t5MEu+y233AW8ADwK3W2qbUhSQiIiKdngLKgCd7tH0c+Lk34YiMXInUtIzXQDvYDrSbbElBCWP8Y86J69ria4ccV7w8T37PSzb37XMYP94ycSLU1Rk2bgxy223tFBUpAc0U8SafnyE28/mPwD8ZY16g+5nPw6kKTkR6198NsuvYG4ffYHLz5AFvnonebJN1c07FTV5kBMoCHjfG7CVWauVSYsttf2KM2dzVSTW342Qtzt69+CorscEgkaVLsVOmeB2VpIlEalr2ZjD3tXh2kx1qXEMxHPXJ+7Ntmx9jOJtstrUZPvGJDmbNsjQ3w4MPZrNnj4+iouErFSJDE+8znz8CfgRgjCkEPg+sQ898iniivxtR17HjbcdxmwfeFDPRm1qyboJe3kxFMshrnV9d9hOrySmD4FRUECgvx501C6eujsBDD9H+la+o1qdQc6aGcDTMpLGT2Fu7l8vyL0t4jFTd1wZTa3MgmfIB8PLlYQoLLY7D2WSzstLHrFlhTp0ytLfHnjWVzBHvM5/vBj5A7JnPvwBaie2Aq2c+RTzQ342o69i3nvsWLbQMaaxk9E/1OCIjmepsJ5dv927c6dPpWLkSGhvJvv9+fDt2ECkr8zo08VjFkQr+eOqPRG2UUEdoUGMk6742HIlhpnwAPHVqd2LZM9lsaoLNmwPk5loWL454GKEkKt7ftieB54GfAl+y1r6VsohEZED9LdNJtCB0qvunehyRka5zl/l3EVttdJa1dqs3EWUoazGNjbjFxWAM5OfjTpiA09DgdWSSBnomjq+feH1QYyTrvjYciWGmfQDcM9mcOzfCunVBWlthzZoOsrO9jk4SEe+y23ekOA4RERE5jzHmTmAtseW2PYsWWEDJZyLCYYhEsH5/d5vfD+3t3sUkacOrD0R720128tjJrJ2/NqWJYSZ9ANzczNlk85ZbOnj00QChEKxe3cHkyVpym2nSd55dREREvgTMsdbu9zqQjOf3g8+HCffYmCQchtxc72KSUa+33WQjNoJjHI8jSw8tLbBhQ3ey+ZOf+KmtdSgsdNm+PZbGFBe7LFgQ9ThSiZeSTxERkfR1AjjgdRAjgjHYvDxMfT1YC42NOMePEykt9ToySRPVp6t54fALNLY39lrT0otSLF1xxVtrc6R57LHAOcnmK684jB0LY8ZAa6sBoLV1gEEkrSj5FMlA/d2Iuo4dbT3KuMC4AW9Sid7UknUTHM03U5EEfAHYaIz5d6C+5wFr7SFvQspckblzCZSXE1i/HhMKYR2HyKJFXoclaSLshglHw2T7sikcW3hBTcuBnsVM1X0tlbU2jzcY9v3RYf0rAbKzLQsXRpg5M32WsublWYqKYjv3t7YarrzSpbg4yoc/rJnOTKXkUyQD9Xcj6jqW789n4tiJA96kEr2pJesm6HXhapEMEQCuBq4/r92iUmcJc+fNo8Pvx1dZiVtYSGT5cpVZEaBz1vPQC8y6eBZhN0x2VvYFieNAm/Sk6r6WilqbXYnyLyvriRLlt2Mfxhy7hN3f+CilpS7Z2aRFMnrDDarfOdL0mXwaY+6LZwBr7T3JC0dE4tHfjajrWFVVFcXFxUMaKxn9Uz2OyAi3DrgLeJxzNxySwTAGd84cOubM8ToSSTPxJI4DbdKTrPvacKwM6rrehXMuIi8PjGlmgr+Jh//B4cwZwwc/GKWuzrBxY5DbbmunqCh9ZkMls/U383npsEUhIiIivfEBj1hrtcZsINbi7N2Lr7ISGwwSWboUO2VK3+0iPaTTB6LDsTKot+utqTHsKHH5yEcifPSjEZqb4cEHs9mzx0dRkWYgJTn6TD6ttTcNZyAiIiJygf8DfMUYc7+1VlMP/XAqKgiUl+POmoVTV0fgoYdo/8pXcPbv77Vdy20lXXmRCHfV0SwstHzoQxEATp0ytLfHnrsUSZaE9nE2xuQaY2YYY4q6vlIVmIiIiHAH8L+AJmPMoZ5fHseVdny7d+NOn07HypW0r1qFcV18O3b02S4iMT3raN56awfZ2d3JaG6uZfHiiNchyggS14ZDxpgrgB8B7yK2yYHp/BO04YGIiEiq3Oh1AGnPWpzKSrKefx47ZQqmrg47ZQruhAk49fWYxkbc4mIwBvLzY+0NDV5HLRloV80uNu/bTF1THS7uiNil/fw6mpMn23OS0TVrYsmoSLLEu9vtOuBXwAeBauAdwAPAztSEJSIiItZaTdENwKmoIPD44xCNYpqaupfV+v2xd9aRCNbv736B3w/t7d4FLBmrsq6SmjM1ADjGGRG7tJ9fR7OjA157zSEvrzsZFUmmeJPPdwEfttaGjTHGWhsyxnwZeA3YkrrwRERERi9jjB+4G/gMMBU4CvwQ+Jq1tsPL2NKFb/du3BkzMAUFuJdcQtYf/xhbVhsOw8UXg8+HCffYLCUchtxc7wKWjHXbu29j9Z+tPltqZdV7Vnkd0pCdX0fzN7/JIhKB6dOjbN8eSxOKi10WLNCeZ5Ic8SafbYAfCAPHjTGXAaeA8akKTCQZas7UsKtmF28cfoPJzZNZMXuF1yElpCv+k+0ncXDOxt9Xu4iMOF8H3gvcDhwEpgNrgTzgix7GlR6s7V5WC5imJtzx43EOHcI5fpxIaSlOUxOmvh6shcbGs+0iiRqo1Ep/4rlve3FvP7+OZn6+n1DIALFkNPZncs9ZXW3YudNHY6NJi1qiMrziTT5/DXwSeBT4T+BpoB14LjVhiSRHxZEK9p/Yz/G247jNrtfhJKwr/qiN4nN8A7aLyIjzCeBd1toTnT+/YYz5HfAySj5js5idy2qjc+cSKC/HHDkC4TBuaSmRRYtwJk4kUF5OYP16TCiEdRwiixZ5Hbl45PwE7wNjPzAs543nvp0O9/bzk9FU2LbNjzEwcSKqJToKxfWbba39ZI8f7yK23DYXeCwVQYkkS1lxGctKl/Gt575FCy1eh5Owrvi7lvgM1C4iI45JsH108fvPLqt1582jw+8n+PWvQ0EBHXfcEdtgqLPdV1mJW1hIZPlylVkZxS5I8MYOz3njuW+PlHv7QDOby5eHKSy0OA6qJToKxbvb7T9aa/8PgLXWpfM5T2PMPwDfSl14IkMzlCUy6aCv+DP9ukQkbuXAz4wxXwUOEVt2ezfwY0+jShfGYPPyYstqAbe4GDtjBpH587FTppzt486ZQ8ecOd2vsxZn7158lZXYYJDIkiWYo0e7f166tPv1MqJ4leDFc98eKff2gWY2p07tTkRVS3T0iXdO/x5iha7PdzdKPkVERFLln4jda79LbMOhI8DjwP/2Mqhhc36S2EtSGOlcbpvIslqnooJAeTnurFk4dXUE77oLxo3DveIKnLq67h1zNUM64gwlwas+Xc2zB56ltrmWiBsZEaVWBiNZM5uqJTo6Of0dNMYsNsYsBrKMMR/s+rnz6++AM8MTpoiIyOhjre2w1t5jrZ1lrR1jrS221q611o6KWiFORQWBrVvBdc8mhYRC5/Rx582j4/rrwXFwCwvPLrftj2/3btzp0+lYuZL2Vatwjh6Fjo6zPxvXje2YK9JD2A3TEmlhfM54CscWjohSK4OxbZufQ4cMfn/3zObbb3c/CTB1aizxhL5nNnvWEr31VtUSHU0Gmvn8Qeef2cCmHu0WOAZ8PhVBiYiIjGbGmPlAmbX2n3s59iDw39baiuGPbHj1TBJpbCT7/vvx7dhBpKysu1Nvy2r7c94Ouc4f/4hTXw+ui6mrw06ZgjthAk5DQwquSDJZSUEJJQUlXofhuaHObLa0wIYNAUIh1RIdjfpNPq21MwCMMZuttZ8dnpBEkqdriczR1qOMC4zLuCUyfS3xKSko0dIfkZHtLmBdH8d2AP8CfHz4wvFAzyTRmNjmQclICnvskOtUVBB4/HEsYFpaupfb+v3QPioml2WYxLNkN1OW9cbzzGbPmc01a86d2XzssQC1tQ6Fha5qiY5C8e52+9nOQtfzgKnW2ieMMWM7jzWnMkCRoehaIpPvz2fi2IkZt0Sm5xIf4Gz8fbWLyIgxG3imj2O/pHtl0sjVI0k8KxlJYY8dcn27d+POmIFTVYUtLOxebhsOQ27u0M4jaen8BO8/D/4n78l6T8oTvHju25l2bx/szGZenqWoKFb+LlW1RCV9xbvb7ZXAT4nV9rwEeAJYBHwO+FTKohMZoq4lMlVVVRR3LrHKJP0t8dHSH5ERLQ8IAL29JfMTK3c2svVIEs9KRlLYtUPusWOYUAj3kktiSa7Phzt+PM6hQzjHjxMpLR3aeSQtnZ/gNbQ0DEuCF8+S3Uxa1juUmc3hqCUq6Sve3W7XA/dYa39ojDnV2bYDeDg1YYmIiIxqfwCuBn7Sy7GrO4+PbD3LqFgLjY1JSwojc+cSePxxnLffxnnzTdypUyEQIGvvXkw4TLS0dMAdcyUznZ/gZeqH017SzKYMRbzJ55/QWduT2GZDWGubjTE5KYlKRERkdPv/gO8ZY7KIbS7kGmMc4K+IlV35B0+jGyaDKaMSD3fePDp8PrLvvx+bm0v73Xdjjh4l+PWvYwsK4toxVyQT9CyL0tg4gb/9W3NOWZTB0MymDEW8yecB4M+Ayq4GY8x7gTdTEJOIiMioZq3daowpBB4DgsaY48AEYo+/3Gut3eZpgMPEnTePDr8fX2UlbmEhkeXLk5MUGoP7538eS2SNwU6Zgh07FjtjBpH58y+oJSqSqbZt82MMTJwIx4/72bgxyG23tVNUNPgEVDObMhTxJp9rgZ8bYzYAAWPMncDtwIqURSYiIjKKWWu/ZYz5PvA+YDxwAthlrW30NrJhlGgZlZ6sxdm7F19lJTYYJLJ06QVJZapmVmVkqzlTw66aXZxsP4mDw4rZKxI6Ppx6lkV55ZVjlJdP6LUsSiI0sylDEe9ut/9jjFlKLNncAUwHlllr96YyOBERkdGsM9Hc7nUcmcipqCBQXo47axZOXV13GZUeM6cpm1mVEa3iSAX7T+wnaqP4nAvfSg90fDj1LIsSCmX1WhZFZDjF/S/CWvt7YFUKYxERERFJCt/u3bjTp9OxciU0NpJ9//34duwgUlbW3SmRmdU4ZlJldCgrLmNZ6TK27NvCgdCBhI97oakJfvKTCeTnW0r//ADlr6fHzKyMPvGWWgkAdwPXAVOBo8DjwNestelbgEgkSSprK9m2fxsNrQ04ODz6sUeT2j+ZS3S6xnrj8BtMbp58dqx0WgYkIpJS1mIaG3GLi8EYyM/HnTABp6Fh0EPGM5Mqo0MgKzCk48OtqyxKW1sLX/5yB78+kT4zszL6JFJqpRS4AzhIbNntXcA04OaBXmyM2QR8DKi31v5pL8cN8B/AR4AWYLm19ndxxiaSck+8/gSvNLxC1I2S5WQlvX8yl+h0jXW87Thus5uSc4iIpLVwOFa70+/vbvP7ob190EPGNZMqkmZ6lkW57rpjTJ48g7IJ6TczK6OHE2e/vwI+Zq192lq731r7NHBtZ3s8HgWW9nP8GqC48+tWYsmuSNq4d8G9PP3JpykdH199uUT7lxWXsXb+WmZdPGsoYZ4z1vSx01N2DhGRtOb3g8+HCffYGCUchmCw/9dZi1NZSWDDBvyPPIKprT3bbhobsZMmJW0mVWQ4dJVFufhiy4svXsQjj/jZsysHx8SbAogkV7y/eXXAmPPacoDaeF5srX0BONlPl2uBzTamArjIGKMHKSRtjAuMS2i2MNH+gaxA0m4EfY2VzHOIiKQ1Y7B5eZj6erAWQiGc48dxJ07s92VORQWBrVvBdc8urSUUwkQiSZ9JFRkOXWVRxoyBtjaH1lajsijiqXjfHf8QeMYY822gBrgUWA1sNsYs7upkrX1ukHFMAw73+Lmms63P5LaqqmqQp0rNOJlkNF4zJOe6G0ONtLW1xT1Wov3rj9VzsuVk0v6OTp68cKxknyPdjNTr6o+uuW/FxcUpjkTS1WDKqPS1tNa+8529z6Tm5qb4KiQdVZ+u5tkDz1LbXEvEjbDp5U1My53GkqIlcR0fTj3LolRV1VNc3Pczyr/+tcOPfxzg1ClDTo7l+us7+OAH3T77iwxGvMnnbZ1/3nVe++2dXwAWKEpGUPFIxhuKqqqqUffGZDReMyTvuvPeziPbzY57rET7T2qfREuoJTl/R4egoKDggrGSeo40Mxp/v3XNIr1LuIyKtbEk1e8n8L3vYYNB3GAwtrT28svPnUltbMQ5fpxIaXyPVsjIEnbDtERaGJ8zHoC2aBtt0ba4j6erBx7IxhjLpElw9Khh7docvva1VhYtyqwEtLra8LOfjScnJ0B2tmXhwggzZ6q8TLqIt87njBTHcYTYbGqXSzrbRERERBLXWxmV/sqlhMOYw4dxGhqILlmCU1dH1p49RDoT1sHMpMrIVFJQQklByaCPe62vmdmvfe0arrzSxeeDhgb45CfH8uSTARYtSv/Euadt2/ycOhWgtBTq6gwbNwa57bZ2ioqUgKaDdNny8qfAGmPM48BcIGStjet5UpHh8MKhF3jklUc42nz+XGwRAAAgAElEQVSUqI1yy1O3MOviWdz5vjuT0j+ZS3S6xjraepRxgXFnxyopKEmbZUAiIl7ot1yK349TV4edMOHsstsxzz6Lc+gQMIiZVJE01dfM7Lvf3T3DeeCAQ0cHjB+feQnb8uVhzpyppbR0HM3N8OCD2ezZ46OoKDzwiyXlhiX5NMZsAz4ATDDG1AD3An4Aa+0G4CliZVbeJFZq5abhiEskXm3RNpojzWf/R90aaaW5ozlp/ZO5RKdrrHx/PhPHTjw7VqYuAxIRSZa4yqUY0/1tzxf3NpMqkoEGmpk9dgzuvTebceMsn/985m2qNXWqpWtbgFOnDO3tsY2XJD0MS/Jprb1ugOOW2AZGImnp6hlXc/WMq1PWP5lLdLrG6u25uHReBiQiklKd5VLc4uLey6WEw7iTJ2OOH+9eWpuVhXvZZd7GLTKMGhpgxYoxtLbCt7/dxkUXeR3R4DU1webNAXJzLYsXR7wORzqly7JbERERkdQJh/svl+L3Yy+7DHfmTHAc3MJCmDePjH73LUlXc6aGXTW7ONl+EgeHFbNXeB1S0hw/Hks8QyH4939v40//NLM2GuqptdVh3bogra2wZk0H2dleRyRdlHyKiIjIyOf3918uxRhsfj4YQ8dtt51dlhsdoDaojFy9JZoVRyrYf2I/URtNqJ53JvjCF3I4dswwebLLunUBAN797iirVmXWs5ItLbBt2yQcB1av7mDyZC25TScj61+NiIiISG+MGbBcSp872tbXexi4eKW3RLOsuIxlpcvYsm8LB0IHvA0wyS66yDJjhktLi+GNNxzCYThzBpYsyaxSJY89FqChwc8732nZvj3291Zc7LJgQdTjyASUfIqIiMgoMVC5lD53tFXyOSr1lmgGsgLeBpVC3/lObBPC++8PYAxMnBh7BjTTSpXk5VkuvbSdMWOgtTW2bVhrq8dByVlKPkVERGRUGLBcina0lR5GcqLZn+XLwxQWWhyHjCxVcsMNYaqq6ikuVimkdKTkU0REREYHJZciA5o6tXuGU6VKJNmUfIqIiIiIJKixvZHy18tH5M63oFIlkhpKPkVERERE4lB9uppnDzxLbXMth88c5u3TbzPWP5biguKBX5xBmptRqRJJCSWfIiIiIiLn6ZloRtwIm17eBEBLpIXxOeOZP20+jnF4+/TbHkeaXC0tsGFDgFBIpUok+ZR8ioiISOaxFmfvXnyVldhgkMjSpdgpU7yOSkaQsBs+m2gCtEXbmJY7jZuLbz6n3+bXNo+osiuPPRagttahsNBNaqmS6mrDzp0+GhsN2dmWhQszq4SLJIeSTxEREck4TkUFgfJy3FmzcOrqCDz0EO1f+cq5u9eKDEFJQQklBSVehzHs8vIsRUUukNxSJdu2+c+WcKmrMxlXwkWSQ8mniIiIZBzf7t2406fTsXIlNDaSff/9+HbsIFJW5nVoIhnthhtiJVVqztSwq2YXJ9tPcgAHGNqGSplewkWSQ8mniIiIZBZrMY2NuMXFYAzk5+NOmIDT0OB1ZCIjRsWRCvaf2E/URvE5Q08ZVMJFQMmniIiIpINEnuEMhyESwfr93W1+P7S3D0+sIvS+IdG03GksKVridWhJUVZcxrLSZWzZtyWpz7SqhMvo5ngdgIiIiIhTUUFg61Zw3bPPcBIK9d7Z7wefDxPusVwvHIZgcHiCFeHcDYkKxxbSFm2jLdrmdVhJE8gK4Jjkpgo9S7jceqtKuIxGSj5FRETEcz2f4WxftQrjuvh27Oi9szHYvDxMfT1YC6EQzvHjuBMnDm/QMqqVFJRwx5w7WPWeVZQVlzExZyJ1zXU8/NLDXoeWlnqWcFm5UiVcRistuxURERFvDeIZzsjcuQTKywmsX48JhbCOQ2TRomEMWqRbsp+PHIlSVcJFMotmPkVEREYhY8wnjDH7jDGuMWaOp8EM4hlOd948Oq6/HhwHt7CQjjvuUJkV8UxZcRlr569l1sWzvA4lbXWVcBkzJlbCJfbldVQy3PTRjIiIyOj0GrAM+J7XgfT5DGdubt+vMQZ3zhw65nibN4tA7PnIkSbZGyp1lXCR0U3Jp4iIyChkrX0dwBjjdSgXPsPZ2Ihz/DiR0lKvI5MRrOZMDc/UPMNzzc/h4LBi9tDqWI40PTdUAkbchkriDSWfIiIiMqCqqqq42gZr7LRpjH/mGdruu4+sM2dobWri6CWX4CbxHEORzGvNBCP1eo+1HuP3J35PKBziD6E/MD44HveMS5bJomrs0K+5/lg9J1tOpu1/v0TiMhiuyb9mSGN4KVPiTJZ0ut7i4uI+jyn5FBERGaGMMf8XKOzl0L9Ya3+SyFjnv5moqqrq9w1GwmbNwpkxg9zKSuxllxFZupSZfdX5HGZJv9Y0N5Kv9+U/vMxJ/0mivigzc2Zy/eTr2d2xmwOhA0m55kntk2gJtaTlf7+R/Pd6vtF0rZBZ16vkU0REZISy1v6l1zHETc9wyjAoKy5jWekytuzbwoHQgaTVsUz285EiI5WSTxEREREZFVK1MZCejxSJj5JPERGRUcgY89fAt4GJwM+NMS9ZazN/msZanL178VVWYoNBIkuXYtNk+a6MXCUFJZQUlHgdhkjaU/IpIiIyCllrnwSe9DqOZHMqKgiUl+POmoVTV0fgoYdo/8pXVANUkq7mTA27anZxsv2kdssViVNyFrqLiIiIpAHf7t2406fTsXIl7atWYVwX344dXoclaeh022nKD5RzMHTw7HOa29/eHvfrK45UsP/Efo6cOUJtc20KIxUZOZR8ioiIyMhgLaaxETtpEhgD+fm4EybgNDR4HZmkierT1Xz/pe9zMHSQcDTMb4//loaWBgrHFib8nGZZcRlr569l1sWzUhixyMiiZbciIiIyMoTDEIlg/f7uNr8f2tu9i0nSSs+NgcbnjGeCncDsd8zm2uJrEx4rVZsXpZvqasPOnT4aGw3Z2ZaFCyPMnGm9DksylJJPERERGRn8fvD5MOFwd1s4DLm53sUkaeX8jYEyqT6iV7Zt82MMTJwIdXWGjRuD3HZbO0VFSkAlcUo+RUREZGQwBpuXh6mvB2uhsRHn+HEipaVeRyaSsZYvD1NYaHEcaG6GBx/MZs8eH0VF4YFfLHIeJZ8iIiIyYkTmziVQXk5g/XpMKIR1HCKLFnkdlkjGmjq1e4bz1ClDezvk5WnWUwZHyaeIiIiMGO68eXT4/fgqK3ELC4ksX64yK5IS1aerefbAs9Q2157dLXda7jSWFGV+udzeNDXB5s0BcnMtixdHvA5HMpSSTxERERk5jMGdM4eOOXO8jkRGuJ6bFwEJ75abSZqbYd26IK2tsGZNB9nZXkckmUrJp4iIiIhIgs7fvGikammBDRsChEKwenUHkydrya0MnpJPERERERHp1WOPBaitdSgsdNm+PZY6FBe7LFgQ9TgyyURKPkVEREREpFd5eZaiIheA1lbT+aeXEUkmU/IpIiIiIiK9uuEGlVSR5FHyKSIiIiLSi5ozNeyq2cXJ9pM4OKyYvcLrkEQympJPEREREZFeVBypYP+J/URtFJ+jt80iQ6V/RSIiIiIivSgrLmNZ6TK27NvCgdCBc45pVlQkcUo+RURERER6EcgK9HlMs6IiiXO8DkBEREREJNOUFZexdv5aZl08y+tQRDKGPqYREREREUlQf7OiItI7JZ8iIiIiIqOAnlMVryn5FBERERHpRfXpap498Cy1zbVE3AibXt7EtNxpLCla4nVog6LnVMVreuZTRERERKQXYTdMS6SF8TnjKRxbSFu0jbZom9dhDZqeUxWv6SMPEREREZFelBSUUFJQ0uuxTJwV1XOq4jUlnyIiIiIiCeo5Kwpk/KyoyHBQ8ikiIiIikqD+ZkVFpHd65lNERERERERSTjOfIiIiIiKjQCY+pyoji5JPEREREZFRQM+piteUfIqIiIiIjAJ6TlW8pmc+RUREREREJOU085kurMXZuxdfZSU2GCSydCl2yhSvoxIREREREUkKzXymCaeigsDWreC6OHV1BB56CEIhr8MSERERGRY1Z2oof72c7730PR5+6WGvwxGRFNDMZ5rw7d6NO306HStXQmMj2fffj2/HDiJlZV6HJiIiIpJyFUcq2H9iP1Ebxed4+xa15kwNu2p2cbL9JA4OK2aviOt11dWGnTt9NDYasrMtCxdGmDnTpjhakcyhmc90YC2msRE7aRIYA/n5uBMm4DQ0eB2ZiIiIyLAoKy5j7fy1zLp41rCcr+ZMDc/UPNPrTGtXInzkzBFqm2vjHnPbNj+HDhn8fqirM2zcGOTtt02yQxfJWJr5TAfhMEQiWL+/u83vh/Z272ISERERGUaBrMCwnq/iSAVvnnmTfF/+BTOtZcVlLCtdxpZ9WzgQOhD3mMuXhykstDgONDfDgw9ms2ePj6KicJKjF8lMSj7Tgd8PPh8m3ON/TOEw5OZ6F5OIiIjICFZWXMaVzpXs7th9QYI52ER46tTuJbanThna2yEvT8tuRboo+UwHxmDz8jD19WAtNDbiHD9OpLTU68hERERERqRAVgDHpOYJtKYm2Lw5QG6uZfHiSErOkWqDfe5VpD9KPtNEZO5cAuXlBNavx4RCWMchsmiR12GJiIiISAKam2HduiCtrbBmTQfZ2V5HNDjptAGUjBz6TUoT7rx5dPj9+CorcQsLiSxfDvn5XoclIiIiMiyqT1fz7IFnqW2uJeJG2PTyJqblTmNJ0RKvQ4tbSwts2BAgFILVqzuYPDlzl9wO9rlXkf4o+UwXxuDOmUPHnDleRyIiIiIy7MJumJZIC+NzxgPQFm2jLdrmSSyDTYQfeyxAba1DYaHL9u2xt9nFxS4LFkSHI+ykGu4NoGR0UPIpIiIiIp4rKSihpKBk2M5Xfbqa8gPlRMdEL0gwB5sI5+VZiopcAFpbTeefqbsGkUyj5FNERERERp2wG6Yt2kZhTiFwboI52ET4hhtUUkWkP0o+RURERGTUKSko4TMzP0NxcbHXoaS14w2GfX90WP9KgOxsy8KFEWbOzNxnWcVbqdlfWkREREREMlb16Wq+/9L3+WXlYU41RvmtfZgXjz3Dxo1B3n7beB2eZCjNfIqIiIiIyDm6nntdOOci8vLAmGYm+Jt49cewZ4+PoiItMZbEKfkUEREREZFz9Pbca02NobI9trGSyGAo+RQRERERSaKaMzXsqtnFyfaTODismL3C65CGrKkJNm8OkJtrWbw44nU4kqGUfKY7a3H27sVXWYkNBoksXYqdMsXrqERERESkDxVHKth/Yj9RG8Xn9P52O5MS1OZmWLcuSGsrrFnTQXa21xFJphq2DYeMMUuNMW8YY940xnyll+PLjTENxpiXOr/+brhiS2dORQWBrVvBdXHq6gg89BCEQl6HJSIiIiJ9KCsuY+38tcy6eFaffboS1CNnjlDbXDuM0SWmpQU2bAgQCsHKlR1MnqwltzJ4w5J8GmOygO8C1wBXANcZY67opesT1trZnV/fH47Y0p1v927c6dPpWLmS9lWrMK6Lb8cOr8MSERERkT4EsgI4pv+32fEkqOngsccC1NY6XHyxZft2H4884ufFF7O8Dksy1HDNfL4XeNNa+7a1tgN4HLh2mM6duazFNDZiJ00CYyA/H3fCBJyGBq8jExEREZEhiCdBTQd5eZaiIpcxY6C11XR+eR2VZKrheuZzGnC4x881wNxe+v2NMWYh8Efgi9baw730AaCqqiopgSVrnFQw4TBT6+tpKSjgVGeck0MhbEsL9UOIO52vOZVG43XrmkcHXXPfVDxeRGRobrhBJVUkedJpw6GfAduste3GmNuAx4DFfXVOxhuKqqqq9H5jYi3BSZPIHz+eCZ1xBvLzoaCA/EHGnfbXnCKj8bp1zaODrllEREQyxXAln0eAS3v8fEln21nW2hM9fvw+8PVhiCu9GYPNy8PU14O10NiIc/w4kdJSryMTERERSYpM2vU1XtWnq3n2wLPUNtcScSNsenkT03KnsaRoidehiXhquJLP3wLFxpgZxJLOTwPX9+xgjJlire3a6qsMeH2YYktrkblzCZSXE1i/HhMKYR2HyKJFXoclIiIikhTxlCXJNGE3TEukhfE54wFoi7bRFm07p48SVBmNhuVfuLU2YoxZA2wHsoBN1tp9xpj7gEpr7U+BO4wxZUAEOAksH47Y0p07bx4dfj++ykrcwkIiy5dDfr7XYYmIiIgkRVlxGctKl7Fl3xYOhA54HU5SlBSUUFJQ0m+feBJUkZFm2D5estY+BTx1Xts9Pb6/E7hzuOLJGMbgzplDx5w5XkciIiIiknSBrIDXIXgingRVZKRJ//2dRUREREREJOMp+RQREREREZGUU/IpIiIiIiIiKTcythQTERERkYykXV9FRg8lnyIiIiLiGe36KjJ6KPkUEREREc9o11eR0UPJ51BZi7N3L77KSmwwSGTpUuyUKX33j0bxP/AAgaefxgaDtN95J9EPf7jvcRIdP9H+IiIiIiIiw0AbDg2RU1FBYOtWcF2cujoCDz0EoVCf/f333EP2ww/H+jc0kHP77bB/f5/jJDp+ov1FRERERESGg5LPIfLt3o07fTodK1fSvmoVxnXx7djRZ//AM8/gTpxI84sv0vTEE2At2f/6r32Ok+j4ifYXEZHRyRjzDWPMH4wxrxhjnjTGXOR1TCK9qTlTQ/nr5Xzvpe/x8EsPex2OiAyBks+hsBbT2IidNAmMgfx83AkTcBoaeu8fjWKam2P9s7LgiiuweXlkHT3a+zj19YmNn2g8IiIymv0S+FNr7VXAH4E7PY5HpFcVRyrYf2I/R84coba51utwRGQIlHwORTgMkQjW7+9u8/uhvb33/qEQuC42GOxu8/li/Xsbp7U1sfETjUdEREYta+0vrLWRzh8rgEu8jEekL2XFZaydv5ZZF8/yOhQRGSJtODQUfj/4fJhwuLstHIbc3N775+eD42B6JoORCIwd2/s448YlNn6i8YiIiMTcDDzRX4eqqqq42kaq0XStkJ7XW3+snpMtJ5MeWzpea6roWkeudLre4uLiPo8p+RwKY7B5eZj6erAWGhtxjh8nUlrae/+sLOzYsbH+0Si88QamsZHI5Zf3OY7T1BT/+InGIyIiI5ox5v8Chb0c+hdr7U86+/wLEAF+1N9Y57+ZqKqq6vcNxkgymq4V0vd6J7VPoiXUktTY0vVaU0HXOnJl0vUq+RyiyNy5BMrLCaxfjwmFsI5DZNGiPvt3LF1K9pYtjF2wANPUBMbQtnYtTijU6zjOxIkJjZ9oPCIiMnJZa/+yv+PGmOXAx4APWWvtsAQlIiKjlpLPIXLnzaPD78dXWYlbWEhk+fLY8to+hO+7D3JyCDz9NO7EibTfeSdccQWutb2Ok+j4ifYXEZHRyRizFPgnYJG1tsXreEREZORT8jlUxuDOmUPHnDnx9c/KInz33YTvvju+cRIdP9H+IiIyWn0HCAK/NMYAVFhrb/c2JJELVZ+u5tkDz1LbXEvEjbDp5U1My53GkqIlXocmIglS8ikiIjIKWWu1dahkhLAbpiXSwvic8QC0Rdtoi7Z5HJWIDIaSTxERERFJWyUFJZQUlHgdhogkgep8ioiIiIiISMop+RQREREREZGUU/IpIiIiIiIiKafkU0RERERERFJOGw4Nlevi27oV//bt2OxsOlavxp09G6JR/A88QODpp7HBIO133kn0wx8Ga3H27sVXWYkNBoksXYqdMqXv8RPtLyIiIiIikoY08zlEvk2byP72t8F1cY4eJeeOO+DwYfz33EP2ww/H2hsayLn9dti/H6eigsDWrbH2ujoCDz0EoVCf4yfaX0REREREJB0p+Rwi/9NPE73sMlp/+ENaNmwA1yW4cSOBZ57BnTiR5hdfpOmJJ8Basv/1X/Ht3o07fTodK1fSvmoVxnXx7djR5/iJ9hcREREREUlHSj6HwnUxjY2xZbCOA5deijt+PM7hw5jmZuykSZCVBVdcgc3LI+vo0Vj/SZPAGMjPx50wAaehoffxrU2sv4iIiIiISJpS8jkULS2YaBQbDHa3+f3Q1ASue267zwft7RCJYP3+c/u3t/c+fjicWH8REREREZE0peRzKMaMwWZlYXomg+EwjBsHjnNueyQCwSD4fJhw+Nz+PZPUnvz+xPqLiIiIiIikKe12OxSOg83Lw9TWguvCkSM4J04QueoqnFdfxdTXQzQKb7yBaWwkcvnlsf719WAtNDbiHD9OpLS09/GNSay/iIiIiIgklbWWpqYmXNf1OpReZWdnE/JoQ1LHcRg3bhzGmLj6K/kcovA115D98MPkfOYzmNOnwXFov/VWXNcle8sWxi5YgGlqAmNoW7sWJxQiUF5OYP16TCiEdRwiixb1OX5k7tyE+ouIiIiISPI0NTURDAYJBAJeh9KrYDBIdna2J+fu6OigqamJ3NzcuPor+RyiyM0305adjX/7dtypU+lYvRouvZTwffdBTg6Bp5/GnTiR9jvvhCuuwLWWDr8fX2UlbmEhkeXLIT+/z/HdefMS6i8iIiIiIsnjum7aJp5eCwQCtLa2xt1fyedQOQ6RG28kcuON57ZnZRG++27Cd999brsxuHPm0DFnTnzjJ9pfREREho+1OHv34qusxAaDRJYuje2CLyIiF9CGQyIiIiKD5FRUENi6FVwXp66OwEMPgUfPXomIpDslnyIiIiKD5Nu9G3f6dDpWrqR91SqM6+LbscPrsERkhDl27Bg333wzs2fPZtGiRXziE5/gzTff5ODBgyxK0X4w3/nOd5g7dy7vf//7KSsr49ChQ0MeU8tuRURERAbDWkxjI25xMRgD+fm4EybgNDR4HZmIeKi62rBzp4/GRkN2tmXhwggzZ9pBj2et5cYbb+S6665j06ZNALz66qvU19czbdq0ZIV9gauuuopf/epXjBkzhh/84Afce++9PPLII0MaUzOfIiIiIoMRDkMkgvX7u9v8fuhZ51tERp1t2/wcOmTw+6GuzrBxY5C3346vFElvXnjhBXw+HzfffPPZtiuvvJL3v//95/Q7ePAg11xzDQsXLmThwoXs3r0bgLq6Oq655hoWLFjA+973Pnbu3Ek0GmXlypW8733v4/3vfz/f/e53LzjvwoULGTNmDABz5szh6NGjg76GLpr5FBERERkMvx98Pkw43N0WDkOcJQdEZGRavjxMYaHFcaC5GR58MJs9e3wUFYUHfnEvXn/9dWbPnj1gv4kTJ/Lkk0+SnZ3NW2+9xS233MLzzz9PeXk5H/rQh/jHf/xHotEoLS0tvPrqq9TW1rJr1y4ATp8+3e/YW7Zs4S//8i8HFX9PSj5FREREBsMYbF4epr4erIXGRpzjx4mUlnodmYh4aOrU7iW2p04Z2tshL2/wy27jFQ6H+fKXv8xrr72G4zi89dZbALznPe9hzZo1hMNhPvrRj3LVVVfxjne8gwMHDvDlL3+ZJUuWsHjx4j7HfeKJJ/j973/Pz3/+8yHHqGW3IiIiIoMUmTsX5+BBAuvXE1y3Dus4RFK0+YeIZJamJti8OUBurmXx4sigx7n88st56aWXBuy3bt06Jk2axIsvvsjzzz9PR0cHAPPnz+epp55i6tSprFq1im3btnHRRRfx4osvsmDBAjZt2sTnP//5Xsd8/vnn+eY3v8m2bdsIBoODvoYuozP5tBanspKJjz+O/5FHMLW1A7/GdfFt2ULOZz5D9ooVOF2/AJEIgdWrGffOdzL2yivxPf54rL2tjTHvfjfjLr6YcQUF+L7whVh7ezs58+czbvx4xk2YgK+rDmg4TPBTn2LcpZcy7h3vwLdhwzmxBjZsiC/WRPuLiIjIoLnz5tFx/fXgOLiFhXTccQfk53sdloh4rLkZ1q0L0toKt97aQXb24MdauHAhHR0dPProo2fbXnvtNXbu3HlOv8bGRiZPnozjODz++ONEo1EADh06xKRJk/jc5z7HZz/7WV5++WVOnDiB67pce+213H333bz88ssXnPfll1/mC1/4Atu2bWPixImDv4AeRmXy2VWTyyRQk8u3aRPZ3/52rI7X0aPk3HEHHD5MYMUKgj/+MbguJhQi5+//HvbsYcx73kNWdXVsGY7rkvPoo/DII+S8//349u2LtUci5HznO/DEEwSXLSPwi1/E2ltayLnrLnjuuYTrh6nemIiIyDAyBnfOHDpuv53wTTdhp0zxOiIR8VhLC2zYECAUgpUrO5g8eWhLbo0xbNmyheeff57Zs2czb9487rvvPiZPnnxOv7/7u79j27ZtzJ8/n6qqKsaOHQtwdobzL/7iL3jyySe5/fbbOXr0KB/72MdYsGABt956K/fee+8F573nnntobm7mc5/7HAsWLODTn/70kK4DRukzn101ueqvuYb8yZPJvv9+fDt2ECkr6/M1/qefJnrZZbT+8Idw5Ahjr7uO4MaN+HfswObm0rR/P/zud+R+/OPk/PM/4xw9igWa6upg2zZyv/hFxt51F6atDWsMTbW18N//Te5ttzH2y1/GhMPY7Gyaqqvh178m92//ljFf+hKRz33ubP0wGhsHjLVnvbF4+ouIiIiISPI89liA2lqHwkKX7dtj6VZxscuCBdFBjzllypRzZj572tFZW3jmzJnnzIZ+9atfBeD666/n+uuvv+B1L7zwQr/n/MlPfjLIaPs2+mY+O2ty2UmT4q/J5bqx10yZAo4Dl16KO348zsGDmPZ2bEEB+Hzw3vdic3LIqq3FQKwtOxtuugkLmNZWjLUQDMa+PvUprDGY5mZMJBLbHc/vh8WLsYEAzsmTicU6mGsTEREREZGkycuzFBW5jBkDra2m88vrqNLD6Jv5HExNrpYWTDSK7fmQrd8fW8ztuueO5TjdY5k+6vmc3+66AFjfeX8dkUhisaremIiIiIiIp264YXAlVUaD0Tfz2VdNrv52bxozBpuVhemZxIXDMHYsOM65Y7lu91i2j/Xd57c7TmycyO1zKHMAABl5SURBVHm7YPl8icU6mGsTEREREREZBqMv+Ty/JlcohHP8OG5/Ozg5Tuw1tbWx5PLwYZwTJ3CnT8cGg5iTJ2OzlHv2YFpbiU6ZgoVYW1sbPPIIBrA5OVhjYjOR7e3wxBMYa7Fjx8ZmPc+ciSWLzz2H6ejALShILNbBXJuIiIiIiMgwGH3LbonV5AqUlzNp2zaCgUBcNbnC11xD9sMPk/OZz2BOnwbHof3WW7E1NQT/538Yd8UVsUQTaP23f2PM8uVkHT3KuMLCs2M0338/Od/5Dr633mLclClnl9s2f+MbBLdsIfDii4ybMQM6a/K0fPObODk5BMrLCaxfjwmFBoy169ri7S8iIiIiIjIcRt/MJ901uWwCNbkiN99M2+c/H6vjNXUqrQ89BJdeSsfDD9P+yU/GZkfz82n9j/+A976Xlt/9juiMGbHnOx2H1uXL4aabaN25k8if/Ems3eejdc0a+NSnaP+v/6Lj6qtj7WPG0Hr//bB4ccL1w1RvTERERERE0tGonPnsqsnVkJ/PRcXF8b3GcYjceCORG288t93no+O736Xj/P7Z2bT8/vcXjhMM0vqb31zY7vfT/sQTXLA1UFf9sDlz4osz0f4iIiIiIpLWjh07xp133snvfvc78vPzmTRpEg888AB+v59PfvKT7N69O+nn/M1vfsOdd97Jvn372LRpE9dee+2QxxydyaeIiIiIiEgK1JypYVfNLk62n8TBYcXsFUMaz1rLjTfeyHXXXcemTZsAePXVV6mvr2fatGnJCLlXl1xyCevWrePb3/520sZU8ikiIiIiIpIkFUcq2H9iP1EbxecMPd164YUX8Pl83HzzzWfbrrzySgAOHjx4tu3gwYPcfvvtNDc3A/CNb3yDuXPnUldXx0033cSZM2eIRqN885vfZO7cuaxZs4aXXnoJYww33HADq1evPue806dPB8BxkvekppJPERERERGRJCkrLmNZ6TK27NvCgdCBIY/3+uuvM3v27AH7TZw4kSeffJLs7GzeeustbrnlFp5//vn/1969R1lVnnke//4OVUUpV0FRrpJGdMh0iBImijLYS0wQNZLpmRhFUJc6iQraWVnihA4TNTc66TVZjkaI0ZTaXiBB245JUDRtgKlGiIggqDGKQbmKYIpSUepynvlj78LDpbhU1TmHqvP7rFXLs/d+z7uffUrqfZ/9vvs9zJs3j7Fjx3LTTTfR2NjIzp07Wb16NZs3b+a5554DoKamptVxHgonn2ZmZmZmZm2kolNFUc5bX1/PtGnTWLNmDZlMhrVr1wIwYsQIpk6dSn19PRdccAHDhw9n8ODBrFu3jmnTpjFu3DjOOeecgsRYkqvdmpmZmZmZtQfDhg1j5cqVBy03a9Ys+vTpQ3V1NQsXLqQu/frGs846i/nz59OvXz+uv/565syZQ8+ePamurmb06NFUVVVxww035PsyACefZmZmZtYBbXh/A/NencfdK+/mnpX3FDscsxYbM2YMdXV13H///bv3rVmzhiVLluxRrra2luOPP55MJsPcuXNpbGwE4O2336ZPnz5cccUVXH755axatYrt27eTzWaZMGECM2bMYNWqVQW5Fk+7zRVB5oUXKFu+nOjcmYbzziP69m1ZXdksZY88QvmCBURlJXVTppA99dTm95uZmZlZm2nrRV/MikUSDz30ENOnT+f222+nsrKSQYMGMXPmzD3KXXPNNUyePJm5c+dy7rnn0qVLFwCqq6u58847KSsro2vXrsyePZtNmzYxZcoUstksALfccss+512xYgWTJk2ipqaGp556ipkzZ7J06dJWXYv/JebILF1Kxbx5ZE86icyWLVTccQe7vvUt6NHjsOsqq6qi8p57aDzpJDKbNnHUjTfy4cMPU7ZgwX73M3BgHq7IzMzMrDS19aIvZofqLzV/4d/X/TubP9xMQ7aBqlVV9O/Wn3F/M67Fdfbt23ePkc9cixYtAmDIkCF7jIbedtttAEycOJGJEyfu877Fixcf8JwjRozglVdeaWHE++dptznKli0je+KJ1F13Hbuuvx5ls5Slv8zDVf7kkzQOGsRHDz7Izp/9DLJZOv/8583uNzMzM7O2U9Gpgozc1bXCq8/Ws7NhJ72P6s0JXU7g48aP+bjx42KHdUTwyGeTCFRbS3boUJCgRw+yxx5L5t13D7+ubDapa9gwyGRg4ECyvXuT2bBh//s3bmz76zEzMzMzs4I7udfJnNzr5GKHcUTy7aAm9fXQ0ECUl3+yr7wcdu06/Lp27kSNjUTnznvW9eGH+9//se+EmJmZmZlZx+bks0l5OZSVofr6T/bV10Nuoniojj6a6NQJ5Sau9fXQpcv+91dWtjxuMzMzMzOzdsDJZxOJ6N4dbd0KEbBjB5lt28ged9zh15XJJHVt3gzZLKxfT2b7drIDBux/f//+bX89ZmZmZiXsLzV/4d6V9/LWjrd2L/qy4M0FxQ7LrKT5mc8cDaefTsW8eVTMno127CAyGRrOPrtFddWPH0/lPfdw1OTJqKYGMhl2fe1ru1e73Xu/mZmZmbWd3EVfAC/6YnYEcPKZI3vGGdSVl1O2fDnZE06g4corW/Q1KwANV13Fx5WVlC9YQLZfP+qmTIGBA5vdb2ZmZmZtx4u+WEfyzjvvMH36dFasWEGPHj3o06cPM2fOpLy8nIsvvphly5a1+Tl37drFtddey8qVK+nVqxdVVVWceOKJrarTyWcuiezIkdSNHNn6ujIZGiZNomHSpEPbb2ZmZmZm7V8EmRdeoGz5cqJzZxrOO4/o27cV1QWTJk3i0ksvpaqqCoDVq1ezdetW+ufx8b0HH3yQnj178uKLL/LYY49x6623ct9997WqTj/zaWZmZmZm1kYyS5dS8cgjkM2S2bKFijvugB07Wlzf4sWLKSsr46qrrtq97zOf+QxnnnnmHuXeeustxo8fz5gxYxgzZszu0dAtW7Ywfvx4Ro8ezahRo1iyZAmNjY1cd911jBo1ijPPPJO77rprn/POnz+fSy+9FIAJEyawaNEiIqLF1wEe+TQzMzMzM2szZcuWkT3xROquuw5qa6n84Q8pW7SIhosualF9r776KqeeeupByx133HE8/vjjVFZWsnbtWq6++moWLlzIvHnzGDt2LDfddBONjY3s3LmT1atXs3nzZp577jkAampq9qlv8+bNu0dWy8rK6N69O++99x69e/du0XWAk08zMzMzM7O2EYFqa8kOHQoS9OhB9thjybz7bt5PXV9fz7Rp01izZg2ZTIa1a9cCMGLECKZOnUp9fT0XXHABw4cPZ/Dgwaxbt45p06Yxbtw4zjnnnLzHB552a2ZmZmZm1jbq66GhgSgv/2RfeTns2tXiKocNG8bKlSsPWm7WrFn06dOH6upqFi5cSF1dHQBnnXUW8+fPp1+/flx//fXMmTOHnj17Ul1dzejRo6mqquKGG27Yp76+ffuyceNGABoaGqitraVXr14tvg5w8mlmZmZmZtY2ysuhrAzV13+yr74eOnducZVjxoyhrq6O+++/f/e+NWvWsGTJkj3K1dbWcvzxx5PJZJg7dy6NjY0AvP322/Tp04crrriCyy+/nFWrVrF9+3ay2SwTJkxgxowZrFq1ap/zjh8/njlz5gDw61//mjFjxiCpxdcBnnZrZmZmZmbWNiSie3e0dStEQG0tmW3baDjllFZUKR566CGmT5/O7bffTmVlJYMGDWLmzJl7lLvmmmuYPHkyc+fO5dxzz6VLly4AVFdXc+edd1JWVkbXrl2ZPXs2mzZtYsqUKWSzWQBuueWWfc47efJkvv71r3PaaadxzDHH7F5ptzWcfJqZmZmZmbWRhtNPp2LePCpmz0Y7dhCZDA1nn92qOvv27bvHyGeuRYsWATBkyJA9RkNvu+02ACZOnMjEiRP3ed/ixYsPeM7KykoeeOCBFka8fwWbdivpPEmvSXpD0rf2c7yzpF+mx5dJGlyo2MzMzMzMzNpC9owzqJs4ETIZsiecQN2NN0KPHsUO64hQkJFPSZ2Au4AvABuA5yU9ERGv5BS7GvhrRJwk6RLgR8BXCxGfmZmZmZlZm5DIjhxJ3ciRxY7kiFOokc/PA29ExJsRUQfMBSbsVWYC0DSu+ygwVq19otXMzMzMzMyOCIV65rM/sD5newNwenNlIqJB0g6gN7BtfxW+/vrrbRJYW9XTnpTiNUNpXrevuTT4mps3dOjQPEdiZmYdXSaToa6ujoqKimKHcsSpq6sjkzn08cx2u+BQW3QoXn/99ZLrmJTiNUNpXrevuTT4ms3MzPKra9eufPDBB3z00UfFDmW/amtr6d69e1HOnclk6Nq16yGXL1TyuREYmLM9IN23vzIbJJUBPYDthQnPzMzMzMxsX5Lo1q1bscNo1tatWxk4cODBCx4BCvXM5/PAUEmfklQBXAI8sVeZJ4Ar0tf/A3g2IqJA8ZmZmZmZmVkeFWTkM32GcyqwAOgEVEXEy5K+CyyPiCeAXwAPSnoDeI8kQTUzMzMzM7MOQO1pcHHHjh3tJ1gzMzui9OjRwyuoHya3u2Zm1hp7t72FmnZrZmZmZmZmJczJp5mZmZmZmeVdu5p2a2ZmZmZmZu2TRz7NzMzMzMws75x8mpmZmZmZWd6VZPIpqUrSVklrih1LoUgaKOkPkl6R9LKkfyh2TPkmqVLSHyWtSq/5tmLHVCiSOkl6UdJvix1LIUhaJ2m1pJWSlhc7nkKQ1FPSo5L+JOlVSaOKHVM+STol/f02/dRK+kax4yp1kr4n6aX0d/K0pH7FjilfJP1z+u/tJUmPS+pZ7JjyRdJX0nYzK2lksePJB0nnSXpN0huSvlXsePKplPq9pdTfba/93JJ85lPSGOAD4F8i4m+LHU8hSOoL9I2IFZK6AS8AX46IV4ocWt5IEtAlIj6QVA5UA/8QEUuLHFreSfomMBLoHhEXFjuefJO0DhgZEduKHUuhSHoA+H8Rca+kCuDoiKgpdlyFIKkTsBE4PSLeKnY8pUxS94ioTV/fCHw6Iq4tclh5IemLwLPpd5f/CCAi/leRw8oLScOALHA3cFNEdKibeunfkD8DXwA2AM8Dl3bUPlEp9XtLqb/bXvu5JTnyGRGLgfeKHUchRcTmiFiRvn4feBXoX9yo8isSH6Sb5elPh7/bImkAcAFwb7FjsfyQ1AMYA/wCICLqSiXxTI0F1jrxLL6mxDPVhQ78NzYino6IhnRzKTCgmPHkU0S8GhGvFTuOPPo88EZEvBkRdcBcYEKRY8qbUur3llJ/t732c0sy+Sx1kgYDpwHLihtJ/qXTT1cCW4FnIqLDXzNwO3AzyV3rUhHA05JekPS1YgdTAJ8C3gXuS6dX3yupS7GDKqBLgDnFDsISkn4gaT1wGfCdYsdTIFcBTxY7CGux/sD6nO0NdNAEpZSVQn+3PfZznXyWGEldgceAb+x1x7pDiojGiDiV5A715yV19OkmFwJbI+KFYsdSYKMjYgQwHpiSTjHqyMqAEcDsiDgN+BDo0M8sNUmnGF8EzCt2LKVC0u8lrdnPzwSAiPh2RAwEHgamFjfa1jnYtaZlvg00kFxvu3Uo12rWXpVKf7c99nPLih2AFU46H/wx4OGI+Ndix1NIEVEj6Q/AeUBHfuD+LOAiSecDlUB3SQ9FxKQix5VXEbEx/e9WSY+TTKlaXNyo8moDsCHnDuejlEjySXKDYUVEvFPsQEpFRJx7iEUfBuYDt+QxnLw62LVKuhK4EBgb7XzRjMP4vXZEG4GBOdsD0n3WAZRif7c99XM98lki0oeSfwG8GhE/KXY8hSDpuKbVCCUdRbKwwJ+KG1V+RcT0iBgQEYNJpiY+29ETT0ld0kUFSKeefpEj/A9va0XEFmC9pFPSXWOBDreYQjMuxVNujxiShuZsTqAD/42VdB7JIw0XRcTOYsdjrfI8MFTSp9LZFJcATxQ5JmsDpdTfba/93JJMPiXNAZ4DTpG0QdLVxY6pAM4CJgPn6JOvKji/2EHlWV/gD5JeImlonomIkvjqkRJzPFAtaRXwR+B3EfFUkWMqhBuAh9P/v08FfljkePIuvbnwBaAk7mS3E/+UTtV8ieTGT4f9WgPgp0A34Jm0Df1ZsQPKF0n/TdIGYBTwO0kLih1TW0oXjpoKLCBZkOZXEfFycaPKnxLr95ZSf7dd9nNL8qtWzMzMzMzMrLBKcuTTzMzMzMzMCsvJp5mZmZmZmeWdk08zMzMzMzPLOyefZmZmZmZmlndOPs3MzMzMzCzvnHyaFYik+yV9v5ljV0qqLnRM6bmbjesA7/kPSae1cRzDJS1pyzrNzMzM7Mjh5NOshSStk3RuseM4HG2R5Er6EvB+RLzYRmEBEBEvATVp/WZmZgBImihpuaQPJG2W9KSk0cWOq1AkhaSTDnC8r6QnJG1Kyw4uXHRmh8fJp5kdrmuBB/NU98PA1/NUt5mZtTOSvgncDvwQOB4YBMwCJhQzriNMFngK+O/FDsTsYJx8WklLRy+nS3pF0l8l3SepMuf4hZJWSqqRtETS8HT/gyQN4G/SO7E3p/vnSdoiaYekxZL+cwvj+k+SnpH0nqTXJF2cc+x+SXdJ+p2k9yUtkzQk5/gX0/fskDRL0iJJ10gaBvwMGJXGXJNzymOaq2+vuCqAc4BFOfs6SfpHSWvT978gaWB6LCRdL+n19Nj3JA1JP8taSb9K62yyEBgrqXNLPjczM+s4JPUAvgtMiYh/jYgPI6I+In4TEdPSMp0l3Z6O+m1KX3dOj/2dpA2Sbpa0NR01/bKk8yX9OW1j/zHnfLdKelTSL9M2a4Wkz+YcHyZpYdoneFnSRTnHDtY2t6hdl7Q4LbYqbbu/uvfnFBHvRMQs4Pm2+uzN8sXJpxlcBowDhgAnAzMAlDzTWEUyEtcbuBt4QlLniJgMvA18KSK6RsSP07qeBIYCfYAVJCN5h0VSF+AZ4JG0nkuAWZI+nVPsEuA24BjgDeAH6XuPBR4FpqcxvwacCRARr5KMWj6XxtzzYPXtx1AgGxEbcvZ9E7gUOB/oDlwF7Mw5Pg74HHAGcDPwc2ASMBD42/S9pDFuBOqBUw7wEZmZWWkYBVQCjx+gzLdJ2pdTgc8Cnydtx1MnpHX0B74D3EPSBn0O+K/A/5b0qZzyE4B5QC+SdvjfJJVLKgd+AzxN0jbfADwsKbe9aq5tbnG7HhFj0uOfTdvuXx7gszA74jn5NIOfRsT6iHiP5I99UzL0NeDuiFgWEY0R8QCwi6SR26+IqIqI9yNiF3Ar8Nn0zu3huBBYFxH3RURD+mzlY8BXcso8HhF/jIgGkgT31HT/+cDL6R3iBuAOYMshnLO5+vbWE3h/r33XADMi4rVIrIqI7TnHfxwRtRHxMrAGeDoi3oyIHSTJ+t4LF72fnsfMzEpbb2Bb2jY15zLguxGxNSLeJUngJuccrwd+EBH1wFzgWOD/pm31y8ArJElrkxci4tG0/E9IEtcz0p+uwD9FRF1EPAv8lpwbqDTflramXTfrUMqKHYDZEWB9zuu3gH7p6xOBKyTdkHO8Iuf4HiR1IklevwIcR/IMBiQN3Y7DiOdE4PS9psWWsedzlrkJ5U6SBpE0tt3XExEhKXeUsjnN1be3vwLd9to3EFh7gLrfyXn90X62T9irfDegBjMzK3XbgWMllR0gAe1H0nY3yW3HAbZHRGP6+qP0v3u3Q7ltXm4bmk3b0Kb61kdENqfsWyQjqk2aa0tb066bdShOPs2S5KnJIGBT+no9yd3S5qagxl7bE0mm65wLrAN6kCRrOsx41gOLIuILh/k+gM3AgKYNScrdZt+YD9cbabX90ymykMQ7hGRUs1Uk9SdJ8F9rbV1mZtbuPUcy4+jLJI+U7M8mkuTu5XQ7tx1vid19AkkZkja0qb6BkjI5Cegg4M+HUGdr2nWzDsXTbs1giqQBknqRPDvS9DzFPcC1kk5XooukCyQ1jfy9A/xNTj3dSBrJ7cDRJCvztcRvgZMlTW56zkTSf0kXDDqY3wGfSRdUKAOmsOfI4jvAgL0W+TlkEVEH/B44O2f3vcD3JA1NP6fhknq3pP603mfTactmZlbC0sczvgPclbZrR6dt4nhJTWstzAFmSDouXffgO8BDrTjt5yT9fdqGfoOkXV8KLCMZkbw5jeHvgC+RTOU9mNa067Bvf2MfShZLbFqsr7NyFk80O5I4+TRLFgB4GniTZPro9wEiYjnwP4GfkoxgvgFcmfO+mSQNXo2km4B/IZmCs5HkGZKlLQkmIt4Hvkiy+MAmkqk4P+KTRuVA791GMu33xyRJ8KeB5SSNJ8CzJHeHt0ja1pL4SBZeyn2e5ifAr0g+w1rgF8BRLaz7MpIVec3MzIiI/0OysN0M4F2SUcSpwL+lRb5P0s69BKwmWezv+6045a+Br5K0+5OBv09X2K0jSTbHA9tIvu7l8oj40yFcQ4vb9dStwANpf+PiZsp8BHyQvv4Tn0wxNjuiKKK1s/DM2i9J64BrIuL3xY4lH9IpQxuAyyLiD21Y738AU9NFE9qqzuEkCzyNaqs6zczMDpWkW4GTImJSsWMx66j8zKdZByNpHMn0oI+AaSTPnLZoFLY5EXFWW9aX1vkSybL6ZmZmZtYBedqtWcczimT68DaSKUJfjghPvzEzMzOzovK0WzMzMzMzM8s7j3yamZmZmZlZ3jn5NDMzMzMzs7xz8mlmZmZmZmZ55+TTzMzMzMzM8s7Jp5mZmZmZmeXd/wdNo4+viztcKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(14, 8));\n",
    "\n",
    "for c in df['target'].value_counts().index.tolist():\n",
    "    df[\n",
    "        df['target'] == c\n",
    "    ].plot(\n",
    "        kind='scatter',\n",
    "        x='petal length (cm)',\n",
    "        y='petal width (cm)',\n",
    "        color=['r', 'g', 'b'][c],\n",
    "        marker=f'${c}$',\n",
    "        s=64,\n",
    "        alpha='0.5',\n",
    "        label=f'Class {c}',\n",
    "        ax=axs[0],\n",
    "    )\n",
    "    \n",
    "for c in df_pca['target'].value_counts().index.tolist():\n",
    "    df_pca[\n",
    "        df_pca['target'] == c\n",
    "    ].plot(\n",
    "        kind='scatter',\n",
    "        x='Component 1',\n",
    "        y='Component 2',\n",
    "        color=['r', 'g', 'b'][c],\n",
    "        marker=f'${c}$',\n",
    "        s=64,\n",
    "        alpha='0.5',\n",
    "        label=f'Class {c}',\n",
    "        ax=axs[1],\n",
    "    )\n",
    "    \n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "n_components = 2\n",
    "\n",
    "scaler = StandardScaler()\n",
    "lda = LinearDiscriminantAnalysis(n_components=n_components)\n",
    "\n",
    "x_scaled = scaler.fit_transform(x)\n",
    "x_lda = lda.fit_transform(x_scaled, y)\n",
    "\n",
    "df_lda = pd.DataFrame(\n",
    "    x_lda,\n",
    "    columns=[f'Component {n}' for n in range(1, n_components+1)]\n",
    ")\n",
    "df_lda['target'] = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/ipykernel_launcher.py:33: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(14, 8));\n",
    "\n",
    "for c in df['target'].value_counts().index.tolist():\n",
    "    df[\n",
    "        df['target'] == c\n",
    "    ].plot(\n",
    "        kind='scatter',\n",
    "        x='petal length (cm)',\n",
    "        y='petal width (cm)',\n",
    "        color=['r', 'g', 'b'][c],\n",
    "        marker=f'${c}$',\n",
    "        s=64,\n",
    "        alpha='0.5',\n",
    "        label=f'Class {c}',\n",
    "        ax=axs[0],\n",
    "    )\n",
    "    \n",
    "for c in df_pca['target'].value_counts().index.tolist():\n",
    "    df_lda[\n",
    "        df_lda['target'] == c\n",
    "    ].plot(\n",
    "        kind='scatter',\n",
    "        x='Component 1',\n",
    "        y='Component 2',\n",
    "        color=['r', 'g', 'b'][c],\n",
    "        marker=f'${c}$',\n",
    "        s=64,\n",
    "        alpha='0.5',\n",
    "        label=f'Class {c}',\n",
    "        ax=axs[1],\n",
    "    )\n",
    "    \n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df_train, df_test = train_test_split(df, test_size=0.4, random_state=22)\n",
    "\n",
    "x_train = df_train[iris.feature_names]\n",
    "x_test  = df_test[iris.feature_names]\n",
    "\n",
    "y_train = df_train['target']\n",
    "y_test  = df_test['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((90, 4), (60, 4), (90,))"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, x_test.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9333333333333333"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "model = DecisionTreeClassifier() # LogisticRegression()\n",
    "model.fit(x_train, y_train)\n",
    "y_pred = model.predict(x_test)\n",
    "\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "n_components = 2\n",
    "\n",
    "scaler = StandardScaler()\n",
    "pca = PCA(n_components=n_components)\n",
    "# pca = KernelPCA(n_components=n_components, kernel='cosine')\n",
    "\n",
    "x_train_scaled = scaler.fit_transform(x_train)\n",
    "x_test_scaled = scaler.transform(x_test)\n",
    "\n",
    "x_train_pca = pca.fit_transform(x_train_scaled)\n",
    "x_test_pca = pca.transform(x_test_scaled)\n",
    "\n",
    "model = DecisionTreeClassifier() # LogisticRegression()\n",
    "model.fit(x_train_pca, y_train)\n",
    "y_pred = model.predict(x_test_pca)\n",
    "\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9833333333333333"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "n_components = 2\n",
    "\n",
    "scaler = StandardScaler()\n",
    "lda = LinearDiscriminantAnalysis(n_components=n_components)\n",
    "\n",
    "x_train_scaled = scaler.fit_transform(x_train)\n",
    "x_test_scaled = scaler.transform(x_test)\n",
    "\n",
    "x_train_lda = lda.fit_transform(x_train_scaled, y_train)\n",
    "x_test_lda = lda.transform(x_test_scaled)\n",
    "\n",
    "model = DecisionTreeClassifier() # LogisticRegression()\n",
    "model.fit(x_train_lda, y_train)\n",
    "y_pred = model.predict(x_test_lda)\n",
    "\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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